[House Hearing, 116 Congress]
[From the U.S. Government Publishing Office]


  ABOUT FACE: EXAMINING THE DEPARTMENT OF HOMELAND SECURITY'S USE OF 
      FACIAL RECOGNITION AND OTHER BIOMETRIC TECHNOLOGIES, PART II

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                                HEARING

                               BEFORE THE

                     COMMITTEE ON HOMELAND SECURITY
                        HOUSE OF REPRESENTATIVES

                     ONE HUNDRED SIXTEENTH CONGRESS

                             SECOND SESSION

                               __________

                            FEBRUARY 6, 2020

                               __________

                           Serial No. 116-60

                               __________

       Printed for the use of the Committee on Homeland Security
                                     

[GRAPHIC NOT AVAILABLE IN TIFF FORMAT]
                                     

        Available via the World Wide Web: http://www.govinfo.gov

                               __________

                              

                    U.S. GOVERNMENT PUBLISHING OFFICE                    
41-450 PDF                  WASHINGTON : 2020                     
          
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                     COMMITTEE ON HOMELAND SECURITY

               Bennie G. Thompson, Mississippi, Chairman
Sheila Jackson Lee, Texas            Mike Rogers, Alabama
James R. Langevin, Rhode Island      Peter T. King, New York
Cedric L. Richmond, Louisiana        Michael T. McCaul, Texas
Donald M. Payne, Jr., New Jersey     John Katko, New York
Kathleen M. Rice, New York           Mark Walker, North Carolina
J. Luis Correa, California           Clay Higgins, Louisiana
Xochitl Torres Small, New Mexico     Debbie Lesko, Arizona
Max Rose, New York                   Mark Green, Tennessee
Lauren Underwood, Illinois           John Joyce, Pennsylvania
Elissa Slotkin, Michigan             Dan Crenshaw, Texas
Emanuel Cleaver, Missouri            Michael Guest, Mississippi
Al Green, Texas                      Dan Bishop, North Carolina
Yvette D. Clarke, New York           Jefferson Van Drew, New Jersey
Dina Titus, Nevada
Bonnie Watson Coleman, New Jersey
Nanette Diaz Barragan, California
Val Butler Demings, Florida
                       Hope Goins, Staff Director
                 Chris Vieson, Minority Staff Director
                           
                           
                           C O N T E N T S

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                                                                   Page

                               Statements

The Honorable Bennie G. Thompson, a Representative in Congress 
  From the State of Mississippi, and Chairman, Committee on 
  Homeland Security:
  Oral Statement.................................................     1
  Prepared Statement.............................................     2
The Honorable Mike Rogers, a Representative in Congress From the 
  State of Alabama, and Ranking Member, Committee on Homeland 
  Security:
  Oral Statement.................................................    29
  Prepared Statement.............................................    30
The Honorable Sheila Jackson Lee, a Representative in Congress 
  From the State of Texas:
  Prepared Statement.............................................     3

                               Witnesses

Mr. John P. Wagner, Deputy Executive Assistant Commissioner, 
  Office of Field Operations, U.S. Customs and Border Protection, 
  U.S. Department of Homeland Security:
  Oral Statement.................................................     6
  Prepared Statement.............................................     8
Mr. Peter E. Mina, Deputy Officer for Programs and Compliance, 
  Office of Civil Rights and Civil Liberties, U.S. Department of 
  Homeland Security:
  Oral Statement.................................................    15
  Prepared Statement.............................................    17
Dr. Charles H. Romine, Ph.D., Director of the Information 
  Technology Laboratory, National Institute of Standards and 
  Technology:
  Oral Statement.................................................    20
  Prepared Statement.............................................    22

                             For the Record

The Honorable Bennie G. Thompson, a Representative in Congress 
  From the State of Mississippi, and Chairman, Committee on 
  Homeland Security:
  Letter From the Electronic Privacy Information Center..........    76
  News Release, U.S. Travel Association..........................    81
The Honorable Mike Rogers, a Representative in Congress From the 
  State of Alabama, and Ranking Member, Committee on Homeland 
  Security:
  White Paper by Security Industry Association...................    59
  Article From the Information Technology and Innovation 
    Foundation (ITIF)............................................    63
The Honorable Sheila Jackson Lee, a Representative in Congress 
  From the State of Texas:
  Article........................................................    74
The Honorable John Katko, a Representative in Congress From the 
  State of New York:
  Letter From Chad F. Wolf, Acting Secretary, U.S. Department of 
    Homeland Security............................................    39

 
  ABOUT FACE: EXAMINING THE DEPARTMENT OF HOMELAND SECURITY'S USE OF 
      FACIAL RECOGNITION AND OTHER BIOMETRIC TECHNOLOGIES, PART II

                              ----------                              


                       Thursday, February 6, 2020

                     U.S. House of Representatives,
                            Committee on Homeland Security,
                                                    Washington, DC.
    The committee met, pursuant to notice, at 10:05 a.m., in 
room 310, Cannon House Office Building, Hon. Bennie G. Thompson 
[Chairman of the committee], presiding.
    Present: Representatives Thompson, Jackson Lee, Langevin, 
Payne, Rice, Correa, Small, Rose, Underwood, Slotkin, Green of 
Texas, Clarke, Titus, Coleman, Barragan; Rogers, McCaul, Katko, 
Walker, Higgins, Lesko, Green of Tennessee, Joyce, and Shaw.
    Chairman Thompson. The Committee on Homeland Security will 
come to order.
    Let me say at the outset a number of our Members are still 
en route from the Prayer Breakfast this morning, and they will 
join us accordingly, the Ranking Member being one of them.
    The committee is meeting today to receive testimony on the 
Department of Homeland Security's use of facial recognition and 
other biometric technologies.
    Without objection, the Chair is authorized to declare the 
committee in recess at any point.
    Good morning. The committee is meeting today to continue 
examining the Department of Homeland Security's use of facial 
recognition technology.
    The committee held Part I of this hearing in July of last 
year, after news that the Department was expanding its use of 
facial recognition for varying purposes, such as confirming the 
identity of travelers, including U.S. citizens.
    As facial recognition technology has advanced, it has 
become the chosen form of biometric technology used by the 
Government and industry.
    I want to reiterate that I am not wholly opposed to the use 
of facial recognition technology, as I recognize that it can be 
a valuable tool to the homeland security and serve as a 
facilitation tool for the Department's various missions.
    But I remain deeply concerned about privacy, transparency, 
data security, and accuracy of this technology, and want to 
ensure those concerns are addressed before the Department 
deploys it any further.
    Last July, I, along with other Members of this committee, 
shared these concerns at our hearings and left this room with 
more questions than answers.
    In December 2019, the National Institute for Standards and 
Technology published a report that confirmed age, gender, and 
racial bias in facial recognition algorithms.
    NIST, for example, found that depending on the algorithm, 
African American and Asian American faces were misidentified 10 
to 100 times more than white faces.
    Although CBP touts that the match rate for this facial 
recognition system is over 98 percent, it is my understanding 
that NIST did not test CBP's current algorithm for its December 
2019 report.
    Moreover, CBP's figures do not account for images of 
travelers who could not be captured due to a variety of 
factors, such as lighting or skin tone, actually making the 
actual match rate significantly lower.
    These findings continue to suggest that some of this 
technology is not really ready for prime time and requires 
further testing before wide-spread deployment.
    Misidentifying even a relatively small percentage of the 
traveling public could affect thousands of passengers annually 
and likely would have a disproportionate effect on certain 
individuals. This is unacceptable.
    Data security also remains an important concern. Last year 
a CBP contractor experienced a significant data breach, which 
included traveler images being stolen.
    We look forward to hearing more about these lessons CBP 
learned from this incident and the steps that it takes to 
ensure that biometric data is kept safe.
    Transparency continues to be key. The American people 
deserve to know how the Department is collecting facial 
recognition data and whether the Department is, in fact, 
safeguarding their rights when deploying such technology.
    That is why we are here 7 months later to continue our 
oversight. I am pleased that we again have witnesses from CBP 
and NIST before us to provide us with an update and answer our 
questions.
    We will also have testimony from DHS's Office of Civil 
Rights and Civil Liberties. This office is charged with 
ensuring the protection of our civil rights and civil liberties 
as it relates to the Department's activities, no easy task, 
especially these days.
    Be assured that under my leadership this committee will 
continue to hold the Department accountable for treating all 
Americans equitably and ensuring that our rights are protected.
    I look forward to a robust discussion with all of the 
witnesses, and I thank the Members for joining us today.
    [The statement of Chairman Thompson follows:]
                Statement of Chairman Bennie G. Thompson
                            February 6, 2020
    The Committee on Homeland Security is meeting today to continue 
examining the Department of Homeland Security's use of facial 
recognition technology. The committee held Part I of this hearing in 
July of last year--after news that the Department was expanding its use 
of facial recognition for varying purposes, such as confirming the 
identities of travelers, including U.S. citizens. As facial recognition 
technology has advanced, it has become the chosen form of biometric 
technology used by the Government and industry. I want to reiterate 
that I am not wholly opposed to the use of facial recognition 
technology, as I recognize that it can be valuable to homeland security 
and serve as a facilitation tool for the Department's varying missions. 
But I remain deeply concerned about privacy, transparency, data 
security, and the accuracy of this technology and want to ensure these 
concerns are addressed before the Department deploys it further.
    Last July, I--along with other Members of this committee--shared 
these concerns at our hearing and left this room with more questions 
than answers. In December 2019, the National Institute for Standards 
and Technology (NIST) published a report that confirmed age, gender, 
and racial bias in some facial recognition algorithms. NIST, for 
example, found that depending on the algorithm, African-American and 
Asian-American faces were misidentified 10 to 100 times more than white 
faces. Although CBP touts that the match rate for its facial 
recognition systems is over 98 percent, it is my understanding that 
NIST did not test CBP's current algorithm for its December 2019 report. 
Moreover, CBP's figure does not account for images of travelers who 
could not be captured due a variety of factors such as lighting or skin 
tone--likely making the actual match rate significantly lower. These 
findings continue to suggest that some of this technology is not ready 
for ``prime time'' and requires further testing before wide-spread 
deployment.
    Misidentifying even a relatively small percentage of the traveling 
public could affect thousands of passengers annually, and likely would 
have a disproportionate effect on certain individuals. This is 
unacceptable. Data security also remains an important concern. Last 
year, a CBP subcontractor experienced a significant data breach, which 
included traveler images being stolen. We look forward to hearing more 
about the lessons CBP learned from this incident and the steps that it 
has taken to ensure that biometric data is kept safe. Transparency 
continues to be key. The American people deserve to know how the 
Department is collecting facial recognition data, and whether the 
Department is in fact safeguarding their rights when deploying such 
technology. That is why we are here 7 months later to continue our 
oversight.
    I am pleased that we again have witnesses from CBP and NIST before 
us to provide us with an update and answer our questions. We will also 
have testimony from DHS's Office for Civil Rights and Civil Liberties. 
This office is charged with ensuring the protection of our civil rights 
and civil liberties as it relates to the Department's activities--no 
easy task, especially these days. Be assured that under my leadership, 
this committee will continue to hold the Department accountable for 
treating all Americans equitably and ensuring that our rights are 
protected.

    Chairman Thompson. Other Members are reminded that 
statements may be submitted for the record.
    [The statement of Honorable Jackson Lee follows:]
               Statement of Honorable Sheila Jackson Lee
                            February 6, 2020
    Thank you, Chairman Thompson and Ranking Member Rogers for holding 
today's important hearing on ``About Face: Examining the Department of 
Homeland Security's Use of Facial Recognition and Other Biometric 
Technologies, Part II.''
    I look forward to hearing from today's Government witnesses on 
DHS's use of facial recognition and other biometric technologies.
    Good morning and welcome to our witnesses:
   Mr. John Wagner, deputy executive assistant commissioner, 
        Office of Field Operations, U.S. Customs and Border Protection 
        (CBP), Department of Homeland Security (DHS),
   Mr. Peter Mina, deputy officer for programs and compliance, 
        Office for Civil Rights and Civil Liberties (CRCL), DHS,
   Dr. Charles H. Romine, director, Information Technology 
        Laboratory, National Institute of Standards and Technology 
        (NIST), Department of Commerce.
    The hearing today provides an opportunity for Members of this 
committee to examine DHS's use of biometric technologies, including 
facial recognition technology, for Government purposes.
    Biometrics is the technical term for body measurements and 
calculations.
    It refers to metrics related to human characteristics such as 
fingerprints, eyes, voice, or other unique features associated with 
people.
    Biometrics authentication is used in computer science as a form of 
identification and access control.
    Facial recognition is one of the most popular biometrics.
    Facial recognition systems are computer-based security systems, 
which are deployed to automatically detect and identify human faces.
    Several DHS components have begun expanding their use of facial 
recognition technology for purposes ranging from identifying travelers 
to general surveillance.
    My admiration and respect for the men and women of DHS as public 
servants who are our Nation's first line of defense against terrorism 
that targets our Nation is well-known.
    Securing our Nation's transportation systems, critical 
infrastructure, and civil government agencies from cyber threats 
requires efficiency and effectiveness of all aspects of recruitment, 
training, and retention of professionals.
    In the last decade, domestic terrorism has become an increasing 
concern in the United States, and these persons are in the United 
States, and not coming from overseas.
    So there needs to be concern when people of color are targets of 
those seeking to do violence to people living within our own Nation's 
borders.
    In 2018, domestic extremists killed at least 50 people in the 
United States, a sharp increase from the 37 extremist-related murders 
documented in 2017, though still lower than the totals for 2015 (70) 
and 2016 (72).
    The 50 deaths made 2018 the fourth-deadliest year on record for 
domestic extremist-related killings since 1970.
    According to an analysis by the Washington Post, between 2010 and 
2017, right-wing terrorists committed a third of all acts of domestic 
terrorism in the United States (92 out of 263), more than Islamist 
terrorists (38 out of 263) and left-wing terrorists (34 out of 263) put 
together.
    Recent unpublished FBI data leaked to the Washington Post in early 
March 2019 reveal that there were more domestic terrorism-related 
arrests than international terrorism-related arrests in both fiscal 
year 2017 and fiscal year 2018.
    From 2009 to 2018 there were 427 extremist-related killings in the 
United States; of those, 73.3 percent were committed by right-wing 
extremists, 23.4 percent by Islamist extremists, and 3.2 percent by 
left-wing extremists.
    In short, 3 out of 4 killings committed by right-wing extremists in 
the United States were committed by white supremacists (313 from 2009 
to 2018).
    The culmination of the 2018 mid-term election was consumed by bombs 
placed in the mail addressed to Democrats.
    The risks posed by terrorism must be weighed aganist the privacy 
and civil liberties concerns raised by the deployment and use of 
biometric idetntification systems including facial recognition.
    Today, DHS components including TSA, CBP, and ICE interact more 
intimately with broad swaths of the public than any other Government 
agency, screening over 2 million passengers every day.
    On July 7, 2019, the New York Times reported that ICE has been 
mining State driver's license records for immigration purposes.
    According to this article at least 3 States that offer driver's 
licenses to undocumented immigrants, ICE officials have requested to 
comb through State repositories of license photos, according to newly-
released documents.
    At least 2 of those States, Utah and Vermont, complied, searching 
their photos for matches, those records show.
    In the third State, Washington, agents authorized administrative 
subpoenas of the Department of Licensing to conduct a facial 
recognition scan of all photos of license applicants, though it was 
unclear whether the State carried out the searches.
    In Vermont, agents only had to file a paper request that was later 
approved by Department of Motor Vehicles employees.
    Over 50 percent of all Americans are included in State Department 
of Motor Vehicle records.
    Members of this Committee understand that several components within 
the Department gather and collect biometric information.
    DHS uses biometrics for the purposes of identity verification, and 
it has looked to increase its use of technologies for such purposes.
    Currently, TSA front-line workers and airline employees manually 
compare the traveler in front of them to the photo identification 
provided.
    TSA seeks to leverage facial recognition technology to automate the 
identity verification process to enhance security effectiveness, 
improve operational efficiency, and streamline the traveler experience.
    TSA has demonstrated an interest in using facial recognition to 
validate the identity of TSA PreCheck passengers who have voluntarily 
provided biometric information to TSA.
    TSA has begun capturing photographs of passengers enrolling or 
renewing enrollments in PreCheck.
    The U.S. Citizenship and Immigration Services (USCIS) has long 
collected fingerprints and pictures of applicants for immigration 
benefits.
    CBP has begun implementing a biometric entry-exit system that 
relies on facial recognition for verifying a traveler's identification, 
including U.S. citizens.
    TSA is interested in using facial recognition to validate the 
identity of TSA PreCheck passengers who have voluntarily provided 
biometric information to TSA and, eventually, to verify passenger 
identity for standard screening, including for domestic travel.
    Beyond confirming an individual's identity, some components have 
been using, or are contemplating the use of, facial recognition 
technology to surveil a crowd of people for law enforcement purposes.
    Since November, the United States Secret Service has been 
conducting a facial recognition pilot on a limited basis at the White 
House to search the faces of individuals visiting the complex or 
passing by on public streets and parks.
    Emails from Immigration and Customs Enforcement (ICE) officials 
became public, which detailed meetings with Amazon over its facial 
recognition platform ``Rekognition'' and its possible use on the 
Southern Border.
    In 2018, this same system was reported to have falsely identified 
28 Members of Congress as having a match to known criminals.
    The committee must fully understand the limitations of facial 
recognition systems.
    Although algorithms may be well-developed and work extremely well, 
if the technology is applied to data that is of poor quality or have 
weak technical standards then the output can be worthless.
    If the underlying technology is not the right match for the 
intended purpose of facial recognition or discernment then the system 
will fail.
    The National Institute of Science (NIST) has done admirable work in 
producing 3 reports on the topic of facial recognition, and I look 
forward to learning more about their work in scoring their performance.
    I gather from their efforts that they provide a technical 
assessment of the facial recognition applications brought to them for 
analysis by other Federal Government agencies.
    NIST does not see the algorithms against their own data set and 
observe the outcomes to assess the performance of facial recognition 
applications.
    Additionally, agency can request facial recognition for any 
vendor--the only requirement is that the agency wait 3 months before 
making a second request.
    Use of facial recognition technology is expanding within and 
outside of the Government, which raises concerns about privacy, civil 
liberties, and accuracy in the application of Federal administrative 
procedures that may affect a range of agency decision making such as 
the right to travel--and extend into applications used to determine 
qualifications for Federal benefits programs such as Social Security, 
Medicare/Medicaid, or Veterans programs.
    These concerns relate to the accuracy, reliability, and fairness to 
those who may be subject to Federal use of facial recognition systems 
are not trivial.
    Collection and storage of facial images can occur with or without 
the consent of data subjects.
    There is no law governing facial image capture for Government 
purposes.
    Biometric facial recognition systems deployed at public gatherings 
can be used to support facial image capture for storage and later use 
without the knowledge or permission of data subjects.
    The ``one to many'' application of facial recognition technology 
involves--taking one image of a person and comparing it to stored 
images of perhaps hundreds or thousands of people to successfully 
identify a person is the ``Holy Grail'' of facial recognition.
    There are law enforcement, National security, defense, and homeland 
security applications that would benefit from the success of accurately 
identifying individuals.
    There are also commercial applications for being able to with a 
high degree of accuracy pick a face out of a crowd.
    Because there is such strong interest in solving the problems of 
face recognition the Congress does need to keep track of developments 
in this area.
    The systems of facial recognition currently available have flaws 
and are not as accurate or reliable as they might become as the 
technology evolves.
    Today, we need laws that govern how Federal biometric systems can 
be deployed and reign in how the data collected might be used.
    The committee needs to know where DHS is getting the images it is 
using and whether third-party vendors allow the agency to avoid Privacy 
Act considerations.
    It is incumbent upon our committee to provide the necessary 
guidance to DHS on how these technologies can be used when 
Constitutionally-protected activities are involved.
    DHS components have proceeded with the acquisition and deployment 
of facial recognition technology with little guidance or oversight from 
the Congress or other Federal entities.
    The topic of today's hearing is important and I thank the Chairman 
for his foresight in bringing today's witnesses before the committee.
    I look forward to the testimony of today's witnesses.
    Thank you.

    Chairman Thompson. I welcome our panel of witnesses. Our 
first witness, Mr. John Wagner, currently serves as the deputy 
executive assistant commissioner for the Office of Field 
Operations, U.S. Customs and Border Protection. In his current 
role, he oversees nearly 30,000 Federal employees and manages 
programs related to immigration, customs, and commercial trade-
related CBP missions.
    Mr. Peter Mina is a deputy officer for programs and 
compliance at the Office of Civil Rights and Civil Liberties. 
Mr. Mina previously served as chief of the Labor and Employment 
Law Division for U.S. Immigration and Customs Enforcement.
    Dr. Charles Romine is the director of the Information 
Technology Laboratory at the National Institute of Standards 
and Technology. In this position, he oversees a research 
program that focuses on testing and interoperability, security, 
usability, and reliability of information systems.
    Without objection, the witnesses' full statements will be 
inserted in the record.
    I now ask each witness to summarize his statement for 5 
minutes, beginning with Mr. Wagner.

    STATEMENT OF JOHN P. WAGNER, DEPUTY EXECUTIVE ASSISTANT 
  COMMISSIONER, OFFICE OF FIELD OPERATIONS, U.S. CUSTOMS AND 
    BORDER PROTECTION, U.S. DEPARTMENT OF HOMELAND SECURITY

    Mr. Wagner. Good morning. Chairman Thompson, Ranking Member 
Rogers, Members of the committee, thank you for the opportunity 
to testify here before you today on behalf of U.S. Customs and 
Border Protection.
    I am looking forward to the opportunity to discuss the 
recent NIST report with you today. Since CBP is using an 
algorithm from one of the highest-performing vendors identified 
in the report, we are confident that our results are 
corroborated with the findings of this report.
    More specifically, the report indicates while there is a 
wide range of performance, of the 189 different algorithms that 
NIST reviewed, the highest-performing algorithms had minimal to 
undetectable levels of demographic-based error rates.
    The report also highlights some of the operational 
variables that impact error rates, such as gallery size, photo 
age, photo quality, numbers of photos of each subject in the 
gallery, camera quality, lighting, human behavior factors. All 
influence the accuracy of an algorithm.
    That is why CBP has carefully constructed the operational 
variables in the deployment of the technology to ensure we can 
attain the highest levels of match rates, which remain in the 
97 to 98 percent range.
    One important note is that NIST did not test the specific 
CBP operational construct to measure the additional impact 
these variables may have, which is why we have recently entered 
into an MOU with NIST to evaluate our specific data.
    But as we build out the Congressionally-mandated biometric-
based entry/exit system, we are creating a system that not only 
meets the security mandate, but also in a way that is cost-
effective, feasible, and facilitative for international 
travelers.
    Identity requirements are not new when crossing the border 
or taking an international flight. Several existing laws and 
regulations require travelers to establish their identity and 
citizenship when entering and departing the United States.
    CBP employs biographic and biometric-based procedures to 
inspect the travel documents presented by individuals to verify 
the authenticity of the document and determine if it belongs to 
the actual person presenting it.
    Again, these are not new requirements. The use of facial 
comparison technology simply automates the process that is 
often done manually today.
    The shortcomings of human manual review in making facial 
comparisons are well-documented. Humans are prone to fatigue, 
sometimes have biases they may not even realize to include own 
race and gender biases.
    Fingerprint biometrics have also documented gaps in their 
performance. There is a small percentage of people that we see 
we cannot capture fingerprints from, and there are studies that 
document this, as well, as well as demographic correlations, 
most notably based on age.
    We are all well aware of the issues of common names when we 
rely on a biographic-based vetting scheme alone. So no one 
system by itself is perfect.
    However, since the United States, along with many other 
countries, put a digital photograph into the electronic chip on 
a passport, it would seem to make prudent sense that the 
technology may be useful in determination of the rightful 
document holder.
    It is more difficult today to forge or alter a legitimate 
passport as security features are more stronger than they were 
10 or 15 years ago, but we are still vulnerable to a person 
using a legitimate document, particularly a U.S. travel 
document, that is real but belongs to someone else.
    Using facial comparison technology to date we have 
identified 252 imposters, to include people using 75 genuine 
U.S. travel documents.
    The privacy continues to be integral to our biometric 
mission. CBP is compliant with the terms of the Privacy Act of 
1974, as amended, the E-Government Act of 2002, the Homeland 
Security Act of 2002, the Paperwork Reduction Act of 1995, and 
departmental policies that govern the collection, use, and 
maintenance of personally-identifiable information.
    CBP recently published updates to the appendices in the 
privacy impact assessment covering this program, and Systems of 
Record notices have been published on the databases to process 
and store the information.
    We have met 3 times with representatives of the privacy 
advocacy community, as well as discussions with the Privacy and 
Civil Liberties Oversight Board, and the DHS Privacy and 
Integrity Advisory Committee.
    In November, CBP submitted to the Office of Management and 
Budget a rulemaking that would solicit public comments on the 
proposed regulatory updates and amendments to the Federal 
regulations.
    One final note is that our private-sector partners, the 
airlines and the airports, must agree to documented specific 
CBP business requirements if they are submitting photographs to 
CBP as part of this process. These requirements include a 
provision that images must be deleted after they are 
transmitted to CBP and may not be retained by the private 
stakeholder.
    After the devastating attacks of September 11, we as a 
Nation asked, ``How can we make sure this never happens 
again?'' As part of that answer, the 9/11 Commission report 
recommended that DHS should complete as quickly as possible a 
biometric entry/exit screening system, and that it was, ``an 
essential investment in National security.''
    CBP is answering that call in carrying out the duties 
Congress has given us by continuing to strengthen its biometric 
efforts along the travel continuum and verifying that people 
are who they say they are.
    I thank you for the opportunity to appear today, and I look 
forward to your questions.
    [The prepared statement of Mr. Wagner follows:]
                  Prepared Statement of John P. Wagner
                            February 6, 2020
    Chairman Thompson, Ranking Member Rogers, and Members of the 
committee, thank you for the opportunity to testify before you on the 
efforts of U.S. Customs and Border Protection (CBP) to better secure 
our Nation by incorporating biometrics into our comprehensive entry-
exit system, and to identify overstays in support of our border 
security mission.
    CBP has received public support for its use of biometrics from the 
International Air Transit Association, the World Travel and Tourism 
Council, and the Department of Commerce Travel and Tourism Advisory 
Board.\1\ With international air travel growing at 4.9 percent per year 
and expected to double by 2031, and with an increasingly complex threat 
posture, CBP must innovate and transform the current travel processes 
to handle this expanding volume. Facial comparison technology will 
enable CBP and travel industry stakeholders to position the U.S. travel 
system as best in class, in turn, driving the continued growth in air 
travel volume.
---------------------------------------------------------------------------
    \1\ International Air Transport Association, ``Resolution: End-to-
end Seamless Travel across Borders Closer to Reality'' (June 2, 2019). 
www.iata.org/en/pressroom/pr/2019-06-02-06/. World Travel & Tourism 
Council, ``Gloria Guevara: `We must act and assign priority and 
resources to biometrics' ''. March 6, 2019. www.wttc.org/about/media-
centre/press-releases/press-releases/2019/we-must-act-and-assign-
priority-and-resources-to-biometrics/. United States Travel and Tourism 
Advisory Board, letter to Commerce Secretary, Wilbur Ross, containing 
challenges and recommendation on U.S. Government-private industry 
partnerships on biometric technology (April 29, 2019). https://
legacy.trade.gov/ttab/docs/TTAB_Biometrics%20Recommenda-
tions%20Letter_042919.pdf.
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    As authorized in several statutes and regulations, CBP is 
Congressionally-mandated to implement a biometric entry-exit system.\2\ 
Prior to the Consolidated and Further Continuing Appropriations Act of 
2013 (Public Law 113-6), which transferred the biometric exit mission 
from the Department of Homeland Security's (DHS) United States Visitor 
and Immigration Status Indicator Technology (US-VISIT) Program within 
the National Protection and Programs Directorate (NPPD) to CBP, the 
U.S. Government and the private sector were developing independent 
biometrics-based schemes for administering the entry-exit program 
responsibilities. These varied and often uncoordinated investments 
relied on multiple biometrics and required complicated enrollment 
processes. Public and private-sector entities developed separate uses 
for biometrics, each with varying privacy risks and accountability 
mechanisms. In 2017, CBP developed an integrated approach to the 
biometric entry-exit system that other U.S. Government agencies with 
security functions, such as TSA, as well as travel industry 
stakeholders such as airlines, airports, and cruise lines, could 
incorporate into their respective mission space.
---------------------------------------------------------------------------
    \2\ Statutes that require DHS to take action to create an 
integrated entry-exit system: Sec. 2(a) of the Immigration and 
Naturalization Service Data Management Improvement Act of 2000 (DMIA), 
P.L. 106-215, 114 Stat. 337; Sec. 110 of the Illegal Immigration Reform 
and Immigrant Responsibility Act of 1996, P.L. 104-208, 110 Stat. 3009-
546; Sec. 205 of the Visa Waiver Permanent Program Act of 2000, P.L. 
106-396, 114 Stat. 1637, 1641; Sec. 414 of the Uniting and 
Strengthening America by Providing Appropriate Tools Required to 
Intercept and Obstruct Terrorism Act of 2001 (USA PATRIOT Act), P.L. 
107-56, 115 Stat. 272, 353; Sec. 302 of the Enhanced Border Security 
and Visa Entry Reform Act of 2002 (Border Security Act), P.L. 107-173, 
116 Stat. 543, 552; Sec. 7208 of the Intelligence Reform and Terrorism 
Prevention Act of 2004 (IRTPA), P.L. 108-458, 118 Stat. 3638, 3817; 
Sec.711 of the Implementing Recommendations of the 9/11 Commission Act 
of 2007, P.L. 110-53, 121 Stat. 266, 338; and Sect. 802 of the Trade 
Facilitation and Trade Enforcement Act of 2015, P.L. 114-125, 130 Stat. 
122, 199. In addition, through the Consolidated Appropriations Act of 
2016 and the Bipartisan Budget Act of 2018, Congress authorized up to 
$1 billion in visa fee surcharges through 2027 to support biometric 
entry/exit. P.L. 114-113 129 Stat. 2242 (December 17, 2015); P.L. 115-
123 132 Stat. 64 (February 9, 2018).
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    CBP offered relevant stakeholders an ``identity as a service'' 
solution that uses facial comparison to automate manual identity 
verification, thereby harmonizing the data collection and privacy 
standards each stakeholder must follow. This comprehensive facial 
comparison service leverages biographic and biometric data, both of 
which are key to support CBP's mission, to fulfill the Congressional 
biometric entry-exit mandate while using the system to support air 
travel, improve efficiency, and increase the efficacy of identity 
verification. CBP has been testing options to leverage biometrics at 
entry and departure, specifically through the use of facial comparison 
technology.\3\ These technologies enhance the manual process used today 
by making it more efficient, accurate, and secure. Using data that 
travelers are already required by statute to provide, the automated 
identity verification process uses facial comparison to identify those 
who are traveling on falsified or fraudulent documents as well as those 
seeking to evade screening. These are the individuals who present 
public safety or National security threats or have overstayed their 
authorized period of admission.
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    \3\ DHS/CBP (November 2018), DHS/CBP/PIA-056 Traveler Verification 
Service. (945.31 KB).
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           previous efforts to launch a biometric exit system
    Prior to the Consolidated and Further Continuing Appropriations Act 
of 2013 (Public Law 113-6), which transferred the biometric exit 
mission from DHS headquarters to CBP, the U.S. Government and the 
private sector were already developing independent biometric solutions 
for administering entry-exit programs. For example, from January 2004 
through May 2007, DHS placed kiosks between security checkpoints and 
airline gates to collect travelers' fingerprint biometrics. The 
traveler had the responsibility to find and use the devices, while 
airports where the kiosks were deployed provided varying degrees of 
support. In 2008, DHS issued a Notice of Proposed Rulemaking (NPRM) 
that proposes commercial air and vessel carriers collect biometric 
information from certain aliens departing the United States and submit 
this information to DHS within a certain time frame. Most comments 
opposed the adoption of the proposed rule, citing cost and feasibility. 
Among other comments was the suggestion that biometrics collection 
should strictly be a Governmental function. The suggestion was made 
that the highly competitive air industry could not support a major new 
process of biometric collection on behalf of the Government, and that 
requiring air carriers to collect biometrics was not feasible and would 
unfairly burden air carriers and airports. Additionally, as directed by 
Congress, from May through June 2009, DHS operated 2 biometric exit 
pilot programs in which CBP used a mobile device to collect biometric 
exit data at departure gates while TSA collected it at security 
checkpoints.
    DHS concluded from the NPRM comments and pilot programs that it was 
generally inefficient and impractical to introduce entirely new 
Government processes into an existing and familiar traveler flow, 
particularly in the air environment. DHS also concluded that the use of 
mobile devices to capture electronic fingerprints would be extremely 
resource-intensive. This information helped frame our concept for a 
comprehensive biometric entry-exit system that would avoid adding new 
processes; utilize existing infrastructure; leverage existing 
stakeholder systems, processes, and business models; leverage passenger 
behaviors and expectations; and utilize existing traveler data and 
existing Government information technology infrastructure.
   cbp's integrated approach to a comprehensive biometric entry-exit 
                                 system
    Leveraging CBP's current authorities, we are executing 
Congressional mandates to create and test an integrated biometric 
entry-exit system using facial comparison technology. This technology 
uses existing advance passenger information along with photographs 
already provided to the Government by international travelers to create 
``galleries'' of facial image templates that correspond with the 
individuals expected on international flights arriving or departing the 
United States. These photographs may be derived from passport 
applications, visa applications, or interactions with CBP at a prior 
border inspection.\4\ Once the gallery is created based on the advance 
information, the biometric comparison technology compares a template of 
a live photograph of the traveler--taken where there is clear 
expectation and authority that a person will need to provide 
documentary evidence of their identity--to the gallery of facial image 
templates.
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    \4\ Department of State, Consular Consolidated System, ``Privacy 
Impact Assessment: Consular Consolidated Database'' (January 29, 2020). 
https://2001-2009.state.gov/documents/organization/93772.pdf.
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    For technical demonstrations at the land border, air entry, and 
some air exit operations, CBP cameras take photographs of travelers. 
These tests have been extended on a voluntary basis to exempt certain 
aliens and U.S. citizens.\5\ Participation provides a more accurate and 
efficient method to verify identity and citizenship. In other air exit 
and seaport demonstrations, CBP does not take the photographs. Instead, 
specified partners, such as commercial air carriers, airport 
authorities, and cruise lines, take photographs of travelers and 
transmit the images to CBP's facial matching service. These partners 
use their own camera operators and technology that meets CBP's 
technical and security requirements. These tests occur on a voluntary 
basis and are consistent with that partner's contractual relationship 
with the traveler.
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    \5\ Under Scope of examination, Alien applicants for admission, 8 
C.F.R.  235.1(f)(1)(ii) and Requirements for biometric identifiers 
from aliens on departure from the United States, 8 C.F.R.  
215.8(a)(1), CBP may require certain aliens to provide biometric 
identifiers to confirm their admissibility or, at specified airports, 
their departure. Some aliens are exempt from the requirement to provide 
biometrics. This includes Canadians, under Sect.101(a)(15)(B), who are 
not otherwise required to present a visa or be issued a Form I-94 or 
Form I-95; aliens younger than 14 or older than 79 on the date of 
admission; aliens admitted A-1, A-2, C-3 (except for attendants, 
servants, or personal employees of accredited officials), G-1, G-2, G-
3, G-4, NATO-1, NATO-2, NATO-3, NATO-4, NATO-5, or NATO-6 visas; and 
certain Taiwan officials and members of their immediate families who 
hold E-1 visas, unless the Secretary of State and the Secretary of 
Homeland Security jointly determine that a class of such aliens should 
be subject to the requirements of paragraph (d)(1)(ii); classes of 
aliens to whom the Secretary of Homeland Security and the Secretary of 
State jointly determine the requirement shall not apply; or an 
individual alien to whom the Secretary of Homeland Security, the 
Secretary of State, or the Director of Central Intelligence determines 
this requirement shall not apply.
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    Biometric entry-exit is not a surveillance program. CBP does not 
use hidden cameras. CBP uses facial comparison technology to ensure a 
person is who they say they are--the bearer of the passport they 
present. This technology provides a seamless way for in-scope travelers 
to meet the requirement to provide biometrics upon departure from the 
United States. Travelers are aware their photos are being taken and 
that they can opt out as described below. CBP uses facial comparison 
technology only where a current identity check already exists. CBP 
works closely with partner air carriers and airport authorities to post 
privacy notices and provide tear sheets for impacted travelers and 
members of the public in close proximity to the cameras and operators, 
whether the cameras are owned by CBP or the partners.
    The imposter threat--or the use of legitimate documents that do not 
belong to the bearer--continues to be a challenge for CBP. U.S. 
passports are the most prized version of an imposter document because--
until recently--there was no biometric comparison between the person 
presenting the document and the owner of the document. As document 
security standards have increased in the past 20 years, it has become 
much more difficult to plausibly forge or alter a legitimate document. 
As a result, those who wish to evade detection seek to use legitimate 
documents that belong to someone else. U.S. citizens are not required 
to provide fingerprint biometrics for entry into the country whereas 
foreign nationals may be required to do so.
    CBP is authorized to require ``in-scope'' aliens to provide 
biometric identifiers.\6\ For entry, CBP uses cameras and facial 
comparison technology during the inspection process. CBP operates 
facial comparison technology pilots at exit in certain land and sea 
ports and some airports.\7\ This technology provides the travel 
industry with the tools to verify traveler identity and transmit 
information to CBP.\8\ We have identified best practices from the prior 
DHS work as well as from our international partners and used them in 
the biometric exit system design to avoid an inefficient two-step 
process that requires multiple biometrics to verify traveler identity.
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    \6\ ``In scope'' aliens may be required to provide biometric 
identifiers to confirm their admissibility, or, at specified airports, 
their departure in accordance with Inspection of Persons Applying for 
Admission, Scope of examination, Alien applicants for admission, 8 
C.F.R.  235.1(f)(1)(ii) and Requirements for biometric identifiers 
from aliens on departure from the United States, 8 C.F.R.  
215.8(a)(1).
    \7\ Requirements for biometric identifiers from aliens on departure 
from the United States, 8 C.F.R.  215.8(a)(1).
    \8\ Numerous statutes require advance electronic transmission of 
passenger and crew member manifests for commercial aircraft and 
commercial vessels. These mandates include, but are not limited to Sec. 
115 of the Aviation and Transportation Security Act (ATSA), P.L. 107-
71, 115 Stat. 597; Passenger manifests, 49 U.S.C.  44909 (applicable 
to passenger and crew manifests for flights arriving in the United 
States); Sec. 402 of the Enhanced Border Security and Visa Entry Reform 
Act of 2002 (EBSVERA), P.L. 107-173, 116 Stat. 543; List of alien and 
citizen passengers arriving and departing, 8 U.S.C.  1221; and 
Examination of merchandise, 19 U.S.C.  1499.
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    CBP understood the need to build a system that all stakeholders 
within the travel continuum could participate in without building their 
own independent system--one that could expand to other mission areas 
outside of the biometric exit process. To address these challenges and 
satisfy the Congressional mandate, we are working closely with our 
partners to integrate biometrics with existing identity verification 
requirements to the extent feasible.\9\ Facial comparison technology 
can match more than 97 percent of travelers through the creation of 
facial galleries.\10\ The match rate is based on the percentage of 
travelers with a valid encounter photo who were successfully matched to 
a gallery photo.\11\
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    \9\ Ibid.
    \10\ Department of Homeland Security Fiscal Year 2018 Entry/Exit 
Overstay Report, https://www.dhs.gov/sites/default/files/publications/
19_0417_fy18-entry-and-exit-overstay-report.pdf.
    \11\ DHS/CBP (November 2018), DHS/CBP/PIA-056 Traveler Verification 
Service. (945.31).
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    While CBP's primary responsibility is National security, we must 
also facilitate legitimate trade and travel. The use of facial 
comparison technology has enabled CBP to not only address a National 
security concern head-on by enhancing identity verification but to 
simultaneously improve the traveler experience throughout the travel 
continuum. CBP engineered a biometric exit solution that gives not only 
CBP, but TSA and industry stakeholders such as airlines and airports, 
the ability to automate manual identity verification. This may include 
departure gates, debarkation (arrival) areas, airport security 
checkpoints, and Federal Inspection Services areas.
    CBP uses only photos collected from cameras deployed specifically 
for this purpose and does not use photos obtained from closed-circuit 
television or other live or recorded video. As the facial comparison 
technology automates the manual identity verification process in place 
today, it allows CBP and its stakeholders to make quicker and more 
informed decisions. In August 2019, CBP and TSA provided this committee 
a comprehensive report on the program that included material on the 
operational and security benefits of the biometric entry-exit system, 
CBP and TSA's efforts to address privacy concerns and potential 
performance differential errors, and a comprehensive description of 
audits performed.\12\
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    \12\ DHS, ``Transportation Security Administration and Customs and 
Border Protection: Deployment of Biometric Technologies, Report to 
Congress'' (August 30, 2019 www.tsa.gov/sites/default/files/
biometricsreport.pdf.
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                            cbp authorities
    As described above, numerous Federal statutes require DHS to create 
an integrated, automated biometric entry and exit system that records 
the arrival and departure of aliens, compares the biometric data to 
verify their identities, and authenticates travel documents. Most 
recently, in 2017, Executive Order 13780 called for the expedited 
completion of the biometric entry-exit data system.\13\ DHS has broad 
authority to control alien travel and to inspect aliens under various 
provisions of the Immigration and Nationality Act of 1952 (INA), as 
amended.\14\ As part of CBP's authority to enforce U.S. immigration 
laws, CBP is responsible for interdicting individuals illegally 
entering or exiting the United States; facilitating and expediting the 
flow of legitimate travelers; and detecting, responding to, and 
interdicting terrorists, drug smugglers, human smugglers, traffickers, 
and other persons who may undermine the security of the United States 
at entry.
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    \13\ Other statues that require DHS to create an integrated entry-
exit system include: Sect. 2(a) of the Immigration and Naturalization 
Service Data Management Improvement Act of 2000 (DMIA), P.L. 106-215, 
114 Stat. 337; Sec. 205 of the Visa Waiver Permanent Program Act of 
2000, P.L. 106-396, 114 Stat. 1637, 1641; and Sec. 414 of the Uniting 
and Strengthening America by Providing Appropriate Tools Required to 
Intercept and Obstruct Terrorism Act of 2001 (USA PATRIOT Act), P.L. 
107-56, 115 Stat. 272, 353.
    \14\ Biometric entry and exit data system, 8 U.S.C.  1365b 
mandates the creation of an integrated and comprehensive system. The 
entry and exit data system shall include a requirement for the 
collection of biometric exit data for all categories of individuals 
required to provide biometric entry data. As a result, if a certain 
category of individuals is required to provide biometrics to DHS on 
entry as part of the examination and inspection process, the same 
category of individuals must be required to provide biometrics on exit 
as well. DHS may require individuals to provide biometrics and other 
relevant identifying information upon entry to, or departure from, the 
United States. Specifically, DHS may control alien entry and departure 
and inspect all travelers under    215(a) and 235 of the INA (8 
U.S.C.  1185, 1225). Aliens may be required to provide fingerprints, 
photographs, or other biometrics upon arrival in, or departure from, 
the United States, and select classes of aliens may be required to 
provide information at any time. See, e.g., INA 214, 215(a), 235(a), 
262(a), 263(a), 264(c), (8 U.S.C. 1184, 1185(a), 1225(a), 1302(a), 
1303(a), 1304(c)); 8 U.S.C.  1365b. Pursuant to  215(a) of the INA (8 
U.S.C.  1185(a)), and Executive Order No. 13323 (December 30, 2003) 
(69 FR 241), the Secretary of Homeland Security, with the concurrence 
of the Secretary of State, has the authority to require aliens to 
provide requested biographic information, biometrics, and other 
relevant identifying information as they depart the United States.
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    To effectively carry out its responsibilities under the INA for 
both arrivals and departures from the United States, CBP must be able 
to conclusively determine if a person is a U.S. citizen or national or 
an alien by verifying that the person is the true bearer of his or her 
travel documentation. CBP is authorized to take and consider evidence 
concerning the privilege of any person to enter, reenter, pass through, 
or reside in the United States, or concerning any matter material or 
relevant to the enforcement or administration of the INA.\15\ A person 
claiming U.S. citizenship must establish that fact to the examining 
officer's satisfaction and must present a U.S. passport or alternative 
documentation.\16\
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    \15\ Powers of immigration officers and employees, 8 U.S.C.  
1357(b).
    \16\ Under Scope of examination, 8 C.F.R.  235.1(b), it is 
generally unlawful for a U.S. citizen to depart or attempt to depart 
from the United States without a valid passport. See also Travel 
control of citizens and aliens, 8 U.S.C.  1185(b); and Passport 
requirement; definitions, 22 C.F.R.  53.1.
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    To further advance the legal framework, CBP is working to propose 
and implement regulatory amendments. CBP is working on a biometric 
entry/exit regulation, which will only impact foreign nationals. In 
November 2019, CBP transmitted its proposed regulation on biometric 
entry/exit to the Office of Management and Budget; we are awaiting 
clearance. The rule will go through the full rulemaking process, which 
includes a public comment period.
           nist facial comparison vendor test: december 2019
    CBP has partnered with the National Institute of Standards and 
Technology (NIST) to explore facial comparison technology capabilities. 
NIST used CBP data that was contained in the OBIM data in its 
conclusions issued in a recent demographic differential study. The 
study supports what CBP has seen in its biometric matching operations--
that when a high-quality facial comparison algorithm is used along with 
high-performing cameras, proper lighting and image quality controls, 
face-matching technology can be highly accurate. To ensure higher 
accuracy rates, as well as efficient traveler processing, CBP compares 
traveler photos to a very small gallery of high-quality images that 
those travelers already provided to the U.S. Government to obtain a 
passport or visa.
    CBP uses only one of the 189 face comparison algorithms evaluated 
by NIST and produced by NEC Corporation. As the report demonstrates, 
NIST confirmed that the NEC algorithm that NIST tested is high-
performing and ranked first or second in most categories evaluated, 
including match performance in galleries that are much bigger than 
those used by CBP.\17\ The NIST performance metrics described in the 
report are consistent with CBP operational performance metrics for 
entry-exit. CBP's operational data continues to show there is no 
measurable differential performance in matching based on demographic 
factors. The NIST report shows a wide range in accuracy across 
algorithm developers, with the most accurate algorithms producing many 
fewer errors and undetectable false positive differentials. Since many 
of the performance rates specified in the report would not be 
acceptable for use in CBP operations, we do not use them.
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    \17\ Face Recognition Vendor Test (FRVT), Part 3: Demographic 
Effects, National Institute of Standards and Technology, U.S. 
Department of Commerce (December 2019), p.8.
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    CBP is committed to implementing the biometric entry exit mandate 
in a way that provides a secure and streamlined travel experience for 
all travelers, and CBP will continue to partner with NIST and use NIST 
research to ensure the continued optimal performance of the CBP face 
comparison service. In the upcoming weeks, CBP will directly provide 
NIST with data for NIST to perform an independent and comprehensive 
scientific analysis of CBP's operational face-matching performance, 
including impacts due to traveler demographics and image quality. NIST 
will provide objective recommendations regarding matching algorithms, 
optimal thresholds, and gallery creation.
                             data security
    There are 4 primary safeguards to secure passenger data, including 
secure encryption during data storage and transfer, irreversible 
biometric templates, brief retention periods, and secure storage. 
Privacy is implemented by design, ensuring data protection through the 
architecture and implementation of the biometric technology. CBP 
prohibits its approved partners such as airlines, airport authorities, 
or cruise lines from retaining the photos they collect as part of the 
entry/exit program for their own business purposes. The partners must 
immediately purge the images following transmittal to CBP, and the 
partner must allow CBP to audit compliance with this requirement. As 
discussed in its comprehensive November 2018 Privacy Impact Assessment 
concerning its facial recognition technology, CBP has developed 
business requirements, or system-wide standards, to document this 
commitment.\18\ Our private-sector partners must agree as a condition 
of participation in the pilots.
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    \18\ DHS/CBP (November 2018), DHS/CBP/PIA-056 Traveler Verification 
Service. (945.31 KB).
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      privacy, transparency, civil rights, and future assessments
    CBP is committed to ensuring that our use of technology sustains 
and does not erode privacy protections. We take privacy very seriously 
and are dedicated to protecting the privacy of all travelers. CBP 
complies with the requirements of the Privacy Act of 1974 and all DHS 
and Government-wide policies.\19\ In accordance with DHS policy, CBP 
uses the Fair Information Practice Principles, or FIPPs, to assess the 
privacy risks and ensure appropriate measures are taken to mitigate 
risks from data collection through the use of biometrics. Our 
partnering stakeholders are also held to the same standards.
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    \19\ Records maintained on individuals, 5 U.S.C.  552(a), P.L. 93-
579, 88 Stat. 1896.
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    CBP strives to be transparent and provide notice to individuals 
regarding the collection, use, dissemination, and maintenance of 
personally identifiable information (PII). When airlines or airports 
partner with CBP on biometric air exit, the public is informed that the 
partner is collecting the biometric data in coordination with CBP. We 
notify travelers at these ports using verbal announcements, signs, and/
or message boards that CBP takes photos for identity verification 
purposes, and they are informed of their ability to opt out. Foreign 
nationals may opt out of providing biometric data to a third party, and 
any U.S. citizen or foreign national may do so at the time of boarding 
by notifying the airline-boarding agent that they would like to opt 
out. The airline would conduct manual identity verification using their 
travel document, and may notify CBP to collect biometrics, if 
applicable.
    If requested, CBP Officers provide a tear sheet with Frequently 
Asked Questions, opt-out procedures, and additional information, 
including the legal authority and purpose for inspection, the routine 
uses, and the consequences for failing to provide information. CBP also 
posts signs informing individuals of possible searches, and the purpose 
for those searches, upon arrival or departure from the United States. 
CBP provides general notification of its biometric exit efforts and 
various pilot programs through Privacy Impact Assessments (PIAs) and 
Systems of Records Notices (SORNs) and through information such as 
Frequently Asked Questions, which are readily available at 
www.cbp.gov.\20\
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    \20\ SORNs associated with CBP's Traveler Verification Service are: 
DHS/CBP-007 Border Crossing Information, DHS/CBP-021 Arrival and 
Departure Information System, DHS/CBP-006 Automated Targeting System, 
DHS/CBP-011 U.S. Customs and Border Protection TECS. https://
www.dhs.gov/system-records-notices-sorns.
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    CBP published a comprehensive PIA concerning its facial recognition 
technology, known as the Traveler Verification Service, in November 
2018. An appendix to that document, published on January 8, 2020, 
explains aspects of CBP's biometric use as well as policies and 
procedures for the collection, storage, analysis, use, dissemination, 
retention, and/or deletion of data.\21\ The PIA and the public notices 
specifically highlight that facial images for arriving and departing 
foreign nationals (and those dual national U.S. citizens traveling on 
foreign documentation) are retained by CBP for up to 2 weeks, not only 
to confirm travelers' identities but also to assure continued accuracy 
of the algorithms and ensure there are no signs of any differential 
performance. As always, facial images of arriving and departing foreign 
nationals are forwarded to the IDENT system for future law enforcement 
purposes, consistent with CBP's authority. As U.S. citizens are not 
within the scope for biometric exit, photos of U.S. citizens used for 
biometric matching purposes are held in secure CBP systems for no more 
than 12 hours after identity verification in case of an extended system 
outage or for disaster recovery.\22\ CBP reduced the retention period 
for U.S. citizen photos to no more than 12 hours as a direct result of 
briefings and consultations with Chairman Thompson.
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    \21\ DHS/CBP (November 2018), DHS/CBP/PIA-056 Traveler Verification 
Service. (945.31 KB).
    \22\ Controls of aliens departing from the United States; 
Electronic visa update system, 8 C.F.R.  215; Inspection of persons 
applying for admission, 8 C.F.R.  235.
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    CBP is committed to transparency in this process as well as to 
improving its public messaging to help the public better understand the 
technology. We welcome the committee's input. CBP collaborates 
regularly with the DHS Privacy Office to ensure compliance with privacy 
laws and policies. The DHS Privacy Office commissioned the DHS Data 
Privacy and Integrity Advisory Committee (DPIAC) to advise the 
Department on best practices for the use of facial comparison 
technology. The DPIAC published its report on February 26, 2019.\23\ 
CBP has implemented or is actively working to implement all of the 
DPIAC recommendations. CBP continues outreach efforts with privacy 
advocacy groups regarding the biometric entry-exit program, most 
recently meeting with them in December 2019. CBP also hosted the 
Privacy and Civil Liberties Oversight Board (PCLOB) for a tour of 
biometric processes at Atlanta/Hartsfield International Airport on 
January 15, 2020.\24\
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    \23\ Report 2019-01 of the DHS Data Privacy and Integrity Advisory 
Committee (DPIAC): Privacy Recommendations in Connection with the Use 
of Facial Recognition Technology, Privacy Recommendations in Connection 
with the Use of Facial Recognition Technology.pdf.
    \24\ The Privacy Civil Rights Oversight Board is an independent, 
bipartisan agency within the Executive branch established by the 
Implementing Recommendations of the 9/11 Commission Act, P.L. 110-53, 
https://www.pclob.gov/. Nextgov, Inside the CBP-Build `Backbone' of 
Atlanta's Biometric Terminal, (January 21, 2020) inside-cbp-built-
backbone-atlantas-biometric-terminal.
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cbp's progress toward implementing a comprehensive biometric entry-exit 
                                 system
Biometric Entry-Exit in the Air Environment
    Facial comparison technology is enhancing the arrivals process, 
enabling more efficient and more secure clearance processes that 
benefit airports, airlines, and travelers with shorter connection times 
and standardized arrival procedures. It is an additional tool to reduce 
imposter threat while increasing the integrity of the immigration 
system. Since initiating the use of facial comparison technology in the 
air environment on a trial basis, CBP has identified 7 imposters, 
including 2 with genuine U.S. travel documents (passport or passport 
card), using another person's valid travel documents to seek entry into 
the United States.\25\
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    \25\ Updated January 7, 2020.
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    CBP is working toward full implementation of biometric exit in the 
air to account for over 97 percent of departing commercial air 
travelers from the United States. Stakeholder partnerships are critical 
for implementing a biometric entry-exit system, and airports, airlines, 
and CBP are collaborating to develop a process that meets our biometric 
entry-exit mandate and airlines' business needs. These partnerships 
help ensure that biometric entry-exit does not have a detrimental 
impact on the air travel industry, and that the technology is useful 
and affordable. Stakeholders have attested that using biometrics could 
lead to faster boarding times, enhanced customer service, better use of 
our CBP staffing, and faster flight clearance times on arrival. 
Engagement with additional stakeholders on how they can be incorporated 
into the comprehensive entry-exit system continues, and CBP is ready to 
partner with any appropriate airline or airport that wishes to use 
biometrics to expedite the travel process for its customers.
Biometric Entry-Exit in the Land Environment
    In the land environment, there are often geographical impediments 
to expanding exit lanes to accommodate adding lanes or CBP-staffed 
booths. The biometric exit land strategy focuses on implementing an 
interim exit capability while simultaneously investigating what is 
needed to implement a comprehensive system over the long term. 
Biometrically verifying travelers who depart at the land border will 
close a gap in the information necessary to complete a nonimmigrant 
traveler's record in CBP's Arrival and Departure Information System, 
and will allow us an additional mechanism to better determine when 
travelers who depart the United States via land have overstayed their 
admission period. Given DHS's desire to implement the use of biometrics 
without negatively affecting cross-border commerce, CBP plans to take a 
phased approached to land implementation.
    Facial comparison technology, similar to what is used in the air 
environment has been deployed at entry operations at the Nogales and 
San Luis POEs in Arizona and at the Laredo and El Paso POEs in Texas. 
CBP plans to expand to additional locations along the Southern Border 
in 2020. By using the facial comparison technology in the land 
environment, CBP has identified 247 imposters, including 45 with 
criminal records and 18 under the age of 18, attempting to enter the 
United States. Additionally, CBP tested ``at speed'' facial biometric 
capture camera technology on vehicle travelers.\26\ From August 2018 to 
February 28, 2019, CBP conducted a technical demonstration on people 
inside vehicles moving less than 20 miles per hour entering and 
departing Anzalduas, Texas.
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    \26\ DHS/CBP (November 2018), DHS/CBP/PIA-056 Traveler Verification 
Service (945.31 KB).
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Biometric Entry-Exit in the Sea Environment
    Similar to efforts in the air environment, CBP is partnering with 
the cruise line industry to use facial biometric processing supported 
by CBP's biometric comparison service in the debarkation points at 
seaports.\27\ Automating identity verification allows us to shift 
officer focus to core law enforcement functions and reallocate 
resources from primary inspections to roving enforcement activities. 
Currently, there are 7 sea entry sites and 5 major cruise lines that 
are operating facial comparison cameras to confirm arriving passenger 
identity on closed-loop cruises, which begin and end in the same city. 
Cruise lines report passenger satisfaction feedback that indicate the 
debarkation process is significantly better than feedback from vessels 
not using the technology during debarkation. CBP continues engagement 
with cruise lines and port authorities to expand the technology to 
other businesses and locations.
---------------------------------------------------------------------------
    \27\ Ibid.
---------------------------------------------------------------------------
                               conclusion
    DHS, in collaboration with the travel industry, is assertively 
moving forward in developing a comprehensive biometric exit system in 
the land, air, and sea environments that replace manual identity checks 
with facial comparison technology. Travelers are well aware that their 
picture is being taken for facial comparison purposes, and they have 
access to both basic and detailed information regarding CBP's 
collection of biometric information. Not only is CBP Congressionally-
mandated to implement a biometric entry-exit system, such a system will 
enhance CBP's ability to accomplish its mission: To safeguard America's 
borders thereby protecting the public from dangerous people and 
materials while enhancing the Nation's global economic competitiveness 
by enabling legitimate trade and travel. CBP's collaborative biometric 
efforts address the recommendations of The 9/11 Commission Report, 
specifically, that security and protection should be shared among the 
various travel checkpoints (ticket counters, gates, and exit controls): 
``By taking advantage of them all, we need not depend on any one point 
in the system to do the whole job.''\28\
---------------------------------------------------------------------------
    \28\ The 9/11 Commission, The 9/11 Commission Report, pp. 385-386, 
http://govinfo.library.unt.edu/911/report/911Report.pdf. (7.22MB).

    Chairman Thompson. Thank you for your testimony.
    I now recognize Mr. Mina to summarize his statement for 5 
minutes.

  STATEMENT OF PETER E. MINA, DEPUTY OFFICER FOR PROGRAMS AND 
 COMPLIANCE, OFFICE OF CIVIL RIGHTS AND CIVIL LIBERTIES, U.S. 
                DEPARTMENT OF HOMELAND SECURITY

    Mr. Mina. Good morning. Chairman Thompson, Ranking Member 
Rogers, and distinguished Members of the committee, thank you 
for the opportunity to appear before you today to discuss the 
Department of Homeland Security's use of facial recognition 
technology.
    DHS's commitment to nondiscrimination in law enforcement 
and screening activities remains an important cornerstone of 
our daily work to secure the homeland.
    I would like to make 3 overarching points in my testimony 
today.
    First, the Office of Civil Rights and Civil Liberties has 
been and continues to be engaged with the DHS operational 
components to ensure use of facial recognition technology is 
consistent with civil right and civil liberties, law, and 
policy.
    Second, operators, researchers, and civil rights policy 
makers must work together to prevent algorithms from leading to 
impermissible biases in the use of facial recognition 
technology.
    Third, facial recognition technology can serve as an 
important tool to increase the efficiency and effectiveness of 
the Department's public protection mission, as well as the 
facilitation of lawful travel.
    But it is vital that these programs utilize technology in a 
way that safeguards our Constitutional rights and values.
    Now, to achieve these 3 points, CRCL, No. 1 influences DHS 
policies and programs throughout their life cycle.
    No. 2, engages with Department offices and components in 
the development of new policies and programs to ensure that 
protection of civil rights and civil liberties is fully 
integrated into their foundation.
    No. 3, monitors operational execution and engages with 
stakeholders in order to provide feedback regarding the impacts 
and consequences of policies and programs.
    Fourth and finally, we investigate complaints and make 
recommendations to DHS components, such as complaints including 
allegations of racial profiling or other impermissible bias.
    CRCL recognizes the potential risks of impermissible bias 
in facial recognition algorithms, as previously raised by this 
committee, and supports rigorous testing and evaluation of 
algorithms used in facial recognition systems to identify and 
mitigate impermissible bias.
    CRCL will continue to support the collaborative 
relationship between the National Institute of Standards and 
Technology, the DHS Science and Technology Directorate, the DHS 
Office of Biometric and Identity Management, and DHS 
components, including U.S. Customs and Border Protection, to 
that end.
    In carrying out its mission, CRCL advised DHS components 
and Department offices by participating in enterprise-level 
groups working on biometric and facial recognition issues.
    Further, CRCL directly engages with DHS components. For 
example, CRCL has regularly engaged CBP on the implementation 
of facial recognition technology and its biometric entry and 
exit program.
    In particular, CRCL advised on policy and implementation of 
appropriate accommodations for individuals wearing religious 
headwear, for individuals with a sincere religious objection to 
being photographed, and for individuals who may have a 
significant injury or disability for whom taking photographs 
may present challenges or not be possible.
    As DHS's facial recognition program has matured and 
evolved, CRCL will be collaborating directly with CBP, S&T, and 
OBIM to address potential civil rights and civil liberties 
impacts.
    Further, CRCL will engage communities with CBP and DHS S&T 
to both inform the public regarding CBP's facial recognition 
programs and address potential concerns.
    Finally, we will continue to evaluate any potential alleged 
violations of civil rights or civil liberties in order to 
further inform our policy advice and strengthen DHS's facial 
recognition programs.
    CRCL understands that successful and appropriate facial 
recognition technology requires on-going oversight and quality 
assurance, initial validation and regular revalidation, and a 
close relationship between the users and oversight offices.
    In this way it can be developed to work properly and 
without impermissible bias when it achieves initial operating 
capability and then continually throughout its entire project 
life cycle.
    At the same time, we will need to work with the operational 
components to ensure that policies and practices evolve so that 
the human part of the equation, the users, are also focused on 
responsible deployment of this technology, working in a manner 
that prevents impermissible bias in DHS activities.
    Again, I thank you for the opportunity to appear before you 
today, and I look forward to answering your questions.
    [The prepared statement of Mr. Mina follows:]
                  Prepared Statement of Peter E. Mina
                            February 6, 2020
    Chairman Thompson, Ranking Member Rogers, and distinguished Members 
of the committee, thank you for the opportunity to appear before you to 
discuss the Department of Homeland Security's (DHS) use of facial 
recognition technology. DHS's commitment to nondiscrimination in law 
enforcement and screening activities remains an important cornerstone 
of our daily work to secure the homeland.
    I would like to make three overarching points in my testimony 
today: (1) The Office for Civil Rights and Civil Liberties (CRCL) has 
been and continues to be engaged with the DHS operational components to 
ensure use of facial recognition technology is consistent with civil 
rights and civil liberties law and policy; (2) operators, researchers, 
and civil rights policy makers must work together to prevent algorithms 
from leading to racial, gender, or other impermissible biases in the 
use of facial recognition technology; and (3) facial recognition 
technology can serve as an important tool to increase the efficiency 
and effectiveness of the Department's public protection mission, as 
well as the facilitation of lawful travel, but it is vital that these 
programs utilize this technology in a way that safeguards our 
Constitutional rights and values. To that end, we welcome the 
opportunity to work with DHS policy makers and operators, Congress, 
academic, and other non-Governmental entities on these important 
issues.
                              introduction
    CRCL supports the DHS mission to secure the Nation while preserving 
individual liberty, fairness, and equality under the law. Established 
by the Homeland Security Act of 2002, CRCL's mission integrates civil 
rights and civil liberties into all DHS activities by:
   Promoting respect for civil rights and civil liberties in 
        policy development and implementation by advising Department 
        leadership and personnel, and State and local partners;
   Communicating with individuals and communities whose civil 
        rights and civil liberties may be affected by Department 
        activities, informing them about policies and avenues of 
        remedy, and promoting appropriate attention within the 
        Department to their experiences and concerns;
   Investigating and resolving civil rights and civil liberties 
        complaints filed by the public regarding Department policies or 
        activities, or actions taken by Department personnel; and
   Leading the Department's equal employment opportunity 
        programs and promoting workforce diversity and merit system 
        principles.
    CRCL is a DHS headquarters office, and the CRCL officer reports 
directly to the Secretary of Homeland Security. CRCL works 
collaboratively with, but independently of, the DHS operational 
components, including U.S. Customs and Border Protection (CBP). CRCL's 
work is not, with limited but important exceptions,\1\ remedial in 
nature.
---------------------------------------------------------------------------
    \1\ CRCL has remedial authority under Section 504 of the 
Rehabilitation Act of 1973, as amended, which states, ``No otherwise 
qualified individual with a disability in the United States . . . 
shall, solely by reason of her or his disability, be excluded from the 
participation in, be denied the benefits of, or be subjected to 
discrimination under any program or activity receiving Federal 
financial assistance or under any program or activity conducted by any 
Executive agency. . . .'' 29 U.S.C.  794.
---------------------------------------------------------------------------
    Pursuant to statutory authorities under 6 U.S.C.  345 and 42 
U.S.C.  2000ee-1, CRCL is responsible for assisting the Department in 
developing, implementing, and periodically reviewing policies and 
procedures to ensure the protection of civil rights and civil 
liberties, including in CBP and other component screening and vetting 
programs.
    In carrying out its statutory mission, CRCL influences DHS policies 
and programs throughout their life cycle. CRCL seeks to engage with 
Department offices and components in the development of new policies 
and programs to ensure that protection of civil rights and civil 
liberties are fully integrated into their foundations. As 
implementation begins, CRCL monitors operational execution and engages 
with stakeholders in order to provide feedback to Department and 
component leadership regarding the impacts or consequences of policies 
and programs. Finally, CRCL investigates complaints and makes 
recommendations to DHS components, often related to the creation or 
modification of policies, or changes to implementation, training, 
supervision, or oversight. Such complaints include allegations of 
racial profiling or other impermissible bias. It is important to note 
that the DHS Office of Inspector General has the right of first refusal 
to investigate allegations submitted to CRCL.
dhs's use of facial recognition technology and crcl's role in oversight
    DHS currently uses facial recognition technology to support CBP's 
Biometric Entry-Exit Program and is researching and testing this 
technology to see if it can be deployed in other mission areas, such as 
identity verification in Transportation Security Administration 
passenger screening. A key goal of the Department's use of facial 
recognition technology is identifying and eliminating, to the extent it 
exists, any impermissible bias based on race and gender. In addition to 
the strong civil rights and civil liberties interest in ensuring 
equality of treatment, the DHS operational components have a compelling 
interest in ensuring the accuracy of this or any tool that assists in 
performing the mission. Improved accuracy and efficiency in the 
Department's data systems results in better performance of all the DHS 
missions they support.
    DHS partnered with the National Institute of Standards and 
Technology (NIST) on the assessment of facial recognition technologies 
to improve data quality and integrity, and ultimately the accuracy of 
the technology, as a means of eliminating such impermissible bias.
   Currently, the DHS Office of Biometric Identity Management 
        (OBIM) is partnering with NIST to develop a face image quality 
        standard that will improve the accuracy and reliability of 
        facial recognition as it is employed at DHS.
   CBP is partnering with NIST to analyze performance impacts 
        due to image quality and traveler demographics and providing 
        recommendations regarding match algorithms, optimal thresholds 
        for false positives, and the selection of photographs used for 
        comparison.
    DHS knows that accuracy and reliability, and the resulting 
operational value of facial recognition technology, varies depending on 
how the technology is employed. Variables include the nature of the 
mission supported, variations in the type and quality of the 
photographs, environmental factors such as lighting, the manner in 
which the match is made, and the type of computer processing, including 
the nature of the algorithms, used to make a match.
    Human factors also matter. Users need to be aware of how the 
technology works, its strengths and weaknesses, and how they can ensure 
the technology functions in a way that complies with all applicable 
laws and DHS policy. In addition to being operational considerations, 
these factors also directly affect the civil rights and civil liberties 
of those individuals who encounter this DHS technology. In short, the 
legal and civil rights and civil liberties policy implications of 
facial recognition technology depend on how the technology is 
implemented.
    CRCL recognizes the potential risks of impermissible bias in facial 
recognition algorithms, as previously raised by this committee. CRCL 
supports rigorous testing and evaluation of algorithms used in facial 
recognition systems to identify and mitigate impermissible bias. CRCL 
will continue to support the collaborative relationship between NIST, 
the DHS Science & Technology Directorate, OBIM, and DHS components to 
that end.
      crcl uses partnerships and data to look beyond the algorithm
    As discussed above, CRCL seeks to ensure civil rights and civil 
liberties protections are incorporated into Department and component 
programs--including the policies and practices that guide DHS use of 
facial recognition technology. Our contribution to DHS working groups 
is one way we fulfill our mission and identify areas that may require 
further engagement.
    CRCL participates in DHS enterprise-level groups working on 
biometric and facial recognition issues, including:
   The DHS Executive Steering Committee for Biometric 
        Capabilities, which provides coordination and guidance to all 
        DHS and component-level programs that are developing or 
        providing biometric capabilities in support of DHS mission 
        objectives. The Steering Committee serves as a forum for cross-
        component collaboration and the sharing of biometric 
        challenges, needs, concepts, best practices, plans and efforts; 
        and
   The Joint Requirements Council's Counter Terrorism and 
        Homeland Threats Portfolio Team, which is made up of component 
        subject-matter experts from the key functional areas within the 
        Department that validate and prioritize requirements and 
        capability gaps, to include those relating to biometrics and 
        screening and vetting functions.
    Another way in which we carry out our role in providing proactive 
advice is through direct engagement with DHS components. For example, 
CRCL has regularly engaged CBP on the implementation of facial 
recognition technology in its Biometric Entry-Exit Program. We have 
viewed live demonstrations of the technology at Dulles International 
Airport and Hartsfield-Jackson Airport in Atlanta. In addition, we 
reviewed and commented on internal procedures, as well as proposed 
regulations. CRCL advised on policy and implementation of appropriate 
accommodations for individuals wearing religious headwear (e.g., 
individuals whose headwear may need to be adjusted to take a 
photograph), for individuals with a sincere religious objection to 
being photographed, and for individuals who may have a significant 
injury or disability and for whom taking photographs may present 
challenges or not be possible. CRCL and the DHS Privacy Office also 
work cooperatively with the components to address and mitigate issues 
such as photograph retention and data sharing.
    We fully anticipate continuing to provide advice and guidance on 
DHS's facial recognition programs as they mature and evolve, whether it 
is through one of the Department's enterprise-level groups or directly 
with the operational components.
    Supporting our advisory role on new or proposed policies or 
programs, I would also like to highlight the distinctive way CRCL uses 
the information and allegations we receive as part of our compliance 
process. In addition to the opening of formal investigations into 
allegations of civil rights or civil liberties violations, when CRCL 
does not open an investigation on an allegation, we use the information 
received to track issues and identify potential patterns of alleged 
civil rights or civil liberties violations that may require further 
review. For CBP vetting operations, this data is used to guide CRCL in 
identifying which policies or programs warrant further investigation to 
more closely examine potentially serious or systemic issues. 
Additionally, CRCL shares data with components annually to provide 
visibility into the civil rights matters CRCL has received, and 
publishes data on complaints in the Annual and Semi-Annual Reports to 
Congress.
crcl's continuing efforts to safeguard civil rights and civil liberties 
                 in dhs's use of emerging technologies
    CRCL recognizes that facial recognition technology and the 
computing that enable it are emerging technologies. They require 
intensive support from all entities involved--operators, NIST and other 
researchers, and oversight offices such as CRCL--to ensure that they 
are compliant with applicable law and policy, including civil rights 
and civil liberties protections, in all phases of development and 
deployment. We understand that successful and appropriate facial 
recognition technology requires on-going oversight and quality 
assurance, initial validation and regular re-validation, and a close 
relationship between the users and oversight offices. In this way, it 
can be developed to work properly and without impermissible bias when 
it achieves initial operating capability, and then continually through 
its entire project life cycle. At the same time, we will need to work 
with the operational components to ensure that policies and practices 
evolve, to ensure that the human part of the equation--the users--are 
also focused on the responsible deployment of this technology, working 
in a manner that consistently prevents impermissible bias in DHS 
activities.
    As these and future projects develop, CRCL will remain engaged with 
advocates, technologists, experts, and Congress to ensure that civil 
rights and civil liberties protections are effective and sufficient.
    Again, I thank you for the opportunity to appear before you today, 
and I look forward to answering your questions.

    Chairman Thompson. Thank you also for your testimony.
    I now recognize Dr. Romine to summarize his statement for 5 
minutes.

    STATEMENT OF CHARLES H. ROMINE, Ph.D., DIRECTOR OF THE 
   INFORMATION TECHNOLOGY LABORATORY, NATIONAL INSTITUTE OF 
                    STANDARDS AND TECHNOLOGY

    Mr. Romine. Thank you, Chairman Thompson, Ranking Member 
Rogers, and Members of the committee.
    I am Chuck Romine, the director of the Information 
Technology Laboratory of the National Institute of Standards 
and Technology, also known as NIST.
    Thank you for the opportunity to appear before you today to 
discuss NIST's role in standards and testing for facial 
recognition technology.
    In the areas of biometrics, NIST has been working with 
public and private sectors since the 1960's. Biometric 
technologies provide a means to establish or verify the 
identity of humans based upon one or more physical or 
behavioral characteristics.
    Face recognition technology compares an individual's facial 
features to available images for verification or identification 
purposes. NIST's work improves the accuracy, quality, 
usability, interoperability, and consistency of identity 
management systems and ensures that U.S. interests are 
represented in the international arena.
    NIST's research has provided state-of-the-art technology 
benchmarks and guidance to industry and to U.S. Government 
agencies that depend upon biometrics recognition technologies.
    NIST's face recognition vendor testing program, or FRVT, 
provides technical guidance and scientific support for analysis 
and recommendations for utilization of face recognition 
technologies to various U.S. Government and law enforcement 
agencies, including the FBI, DHS, CBP, and IARPA.
    The NIST FRVT Interagency Report 8280 released in December 
2019 quantified the accuracy of face recognition algorithms for 
demographic groups defined by sex, age, and race or country of 
birth for both one-to-one and one-to-many identification search 
algorithms. It found empirical evidence for the existence of 
demographic differentials in facial recognition algorithms that 
NIST evaluated.
    The report distinguishes between false positive and false 
negative errors and notes that the impacts of errors are 
application-dependent.
    NIST conducted tests to quantify demographic differences 
for 189 face recognition algorithms from 99 developers using 4 
collections of photographs with 18.27 million images of 8.49 
million people.
    These images came from operational databases provided by 
the State Department, the Department of Homeland Security, and 
the FBI.
    I will first address one-to-one verification applications. 
There, false positive differentials are much larger than those 
related to false negative and exist across many of the 
algorithms tested. False positives might present a security 
concern to the system owner as they may allow access to 
imposters.
    Other findings are that false positives are higher in women 
than in men and are higher in the elderly and the young 
compared to middle-aged adults.
    Regarding race, we measured higher false positive rates in 
Asian and African American faces relative to those of 
Caucasians. There are also higher false positive rates in 
Native Americans, American Indian, Alaskan Indian, and Pacific 
Islanders.
    These effects apply to most algorithms, including those 
developed in Europe and the United States. However, a notable 
exception was for some algorithms developed in Asian countries. 
There was no such dramatic difference in false positives in 
one-to-one matching between Asian and Caucasian faces for the 
algorithms developed in Asia.
    While the NIST study did not explore the relationship 
between cause and effect, one possible connection and an area 
for research is the relationship between algorithm's 
performance and the data used to train the algorithm itself.
    I will now comment on one-to-many search algorithms. Again, 
the impact of errors is application-dependent. False positives 
in one-to-many search are particularly important because the 
consequences could include false accusations.
    For most algorithms, the NIST study measured higher false 
positive rates in women, African Americans, and particularly in 
African American women. However, the study found that some one-
to-many algorithms gave similar false positive rates across 
these specific demographics. Some of the most accurate 
algorithms fell into this group.
    This last point underscores one overall message of the 
report: Different algorithms perform differently.
    Indeed, all of our FRVT reports note wide variations in 
recognition accuracy across algorithms, and an important result 
from the demographic study is that demographic effects are 
smaller with more accurate algorithms.
    NIST is proud of the positive impact it has had in the last 
60 years on the evolution of biometrics capabilities. With 
NIST's extensive experience and broad expertise both in its 
laboratories and in successful collaborations with the private 
sector and other Government agencies, NIST is actively pursuing 
the standards and measurement research necessary to deploy 
interoperable, secure, reliable, and usable identity management 
systems.
    Thank you for the opportunity to testify on NIST's 
activities in facial recognition and identity management, and I 
would be happy to answer any questions you may have.
    [The prepared statement of Dr. Romine follows:]
                Prepared Statement of Charles H. Romine
                            February 6, 2020
                              introduction
    Chairman Thompson, Ranking Member Rogers, and Members of the 
committee, I am Chuck Romine, director of the Information Technology 
Laboratory (ITL) at the Department of Commerce's National Institute of 
Standards and Technology (NIST). ITL cultivates trust in information 
technology and metrology through measurements, standards, and testing. 
Thank you for the opportunity to appear before you today to discuss 
NIST's role in standards and testing for facial recognition technology.
              biometric and facial recognition technology
    Home to 5 Nobel Prizes, with programs focused on National 
priorities such as advanced manufacturing, the digital economy, 
precision metrology, quantum science, and biosciences, NIST's mission 
is to promote U.S. innovation and industrial competitiveness by 
advancing measurement science, standards, and technology in ways that 
enhance economic security and improve our quality of life.
    In the area of biometrics, NIST has been working with public and 
private sectors since the 1960's. Biometric technologies provide a 
means to establish or verify the identity of humans based upon one or 
more physical or behavioral characteristics. Examples of physical 
characteristics include face, fingerprint, and iris images. An example 
of behavioral characteristic is an individual's signature. Used with 
other authentication technologies, such as passwords, biometric 
technologies can provide higher degrees of security than other 
technologies employed alone. For decades, biometric technologies were 
used primarily in homeland security and law enforcement applications, 
and they are still a key component of these applications. Over the past 
several years, the marketplace for biometric solutions has widened 
significantly and today includes public and private-sector applications 
world-wide, including physical security, banking, and retail 
applications. According to one industry estimate, the biometrics 
technology market size will be worth $59.31 billion by 2025.\1\ There 
has been a considerable rise in development and adoption of facial 
recognition, detection, and analysis technologies in the past few 
years.
---------------------------------------------------------------------------
    \1\ https://www.grandviewresearch.com/industry-analysis/biometrics-
industry.
---------------------------------------------------------------------------
    Face detection technology determines whether the image contains a 
face. Face analysis technology aims to identify attributes such as 
gender, age, or emotion from detected faces. Face recognition 
technology compares an individual's facial features to available images 
for verification or identification purposes. Verification or ``one-to-
one'' matching confirms a photo matches a different photo of the same 
person in a database or the photo on a credential, and is commonly used 
for authentication purposes, such as unlocking a smartphone or checking 
a passport. Identification or ``one-to-many'' search determines whether 
the person in the photo has any match in a database and can be used for 
identification of a person.
    Accuracy of face recognition algorithms is assessed by measuring 
the two classes of error the software can make: False positives and 
false negatives. A false positive means that the software wrongly 
considered photos of 2 different individuals to show the same person, 
while a false negative means the software failed to match 2 photos 
that, in fact, do show the same person.
       nist's role in biometric and facial recognition technology
    NIST responds to Government and market requirements for biometric 
standards, including facial recognition technologies, by collaborating 
with other Federal agencies, law enforcement, industry, and academic 
partners to:
   research measurement, evaluation, and interoperability to 
        advance the use of biometric technologies including face, 
        fingerprint, iris, voice, and multi-modal techniques;
   develop common models and metrics for identity management, 
        critical standards, and interoperability of electronic 
        identities;
   support the timely development of scientifically valid, fit-
        for-purpose standards; and
   develop the required conformance testing architectures and 
        testing tools to test implementations of selected standards.
    NIST's work improves the accuracy, quality, usability, 
interoperability, and consistency of identity management systems and 
ensures that United States interests are represented in the 
international arena. NIST research has provided state-of-the-art 
technology benchmarks and guidance to industry and to U.S. Government 
agencies that depend upon biometrics recognition technologies.
    Under the provisions of the National Technology Transfer and 
Advancement Act of 1995 (Public Law 104-113) and OMB Circular A-119, 
NIST is tasked with the role of encouraging and coordinating Federal 
agency use of voluntary consensus standards in lieu of Government-
unique standards, and Federal agency participation in the development 
of relevant standards, as well as promoting coordination between the 
public and private sectors in the development of standards and in 
conformity assessment activities. NIST works with other agencies to 
coordinate standards issues and priorities with the private sector 
through consensus standards developing organizations such as the 
International Committee for Information Technology Standards (INCITS), 
Joint Technical Committee 1 of the International Organization for 
Standardization/International Electrotechnical Commission (ISO/IEC), 
the Organization for the Advancement of Structured Information 
Standards (OASIS), IEEE, the Internet Engineering Task Force (IETF), 
and other standards organizations such as the International Civil 
Aviation Organization (ICAO), and the International Telecommunication 
Union's Standardization Sector (ITU-T). NIST leads National and 
international consensus standards activities in biometrics, such as 
facial recognition technology, but also in cryptography, electronic 
credentialing, secure network protocols, software and systems 
reliability, and security conformance testing--all essential to 
accelerate the development and deployment of information and 
communication systems that are interoperable, reliable, secure, and 
usable.
    Since 2010, NIST has organized the biennial International Biometric 
Performance Testing Conference. This series of conferences accelerates 
adoption and effectiveness of biometric technologies by providing a 
forum to discuss and identify fundamental, relevant, and effective 
performance metrics, and disseminating best practices for performance 
design, calibration, evaluation, and monitoring.
                facial recognition tests and evaluations
    For more than a decade, NIST biometric evaluations have measured 
the core algorithmic capability of biometric recognition technologies 
and reported the accuracy, throughput, reliability, and sensitivity of 
algorithms with respect to data characteristics, for example, noise or 
compression, and to subject characteristics, for example, age or 
gender. NIST biometric evaluations advance the technology by 
identifying and reporting gaps and limitations of current biometric 
recognition technologies. NIST evaluations advance measurement science 
by providing a scientific basis for ``what to measure'' and ``how to 
measure.'' NIST evaluations also facilitate development of consensus-
based standards by providing quantitative data for development of 
scientifically sound, fit-for-purpose standards.
    NIST conducted the Face Recognition Grand Challenge (2004-2006) and 
Multiple Biometric Grand Challenge (2008-2010) programs to challenge 
the facial recognition community to break new ground solving research 
problems on the biometric frontier.
    Since 2000, NIST's Face Recognition Vendor Testing Program (FRVT) 
has assessed capabilities of facial recognition algorithms for one-to-
many identification and one-to-one verification. Participation in FRVT 
is open to any organization world-wide. There is no charge for 
participation, and being an on-going activity, participants may submit 
their algorithms on a continuous basis. The algorithms are submitted to 
NIST by corporate research and development laboratories and a few 
universities. As prototypes, these algorithms are not necessarily 
available as mature integrable products. For all algorithms that NIST 
evaluates, NIST posts performance results on its FRVT website and 
identifies the algorithm and the developing organization.
    NIST and the FRVT program do not train face recognition algorithms. 
NIST does not provide training data to the software under test, and the 
software is prohibited from adapting to any data that is passed to the 
algorithms during a test.\2\
---------------------------------------------------------------------------
    \2\ The process of training a face recognition algorithm (or any 
machine learning algorithm) involves providing a machine learning 
algorithm with training data to learn from. The training data shall 
contain the correct answer, which is known as ground-truth label, or a 
target. The learning algorithm finds patterns in the training data that 
map the input data attributes to the target and builds a machine-
learning model that captures these patterns. This model can then be 
used to get predictions on new data for which the target is unknown.
---------------------------------------------------------------------------
    NIST provides technical guidance and scientific support for 
analysis and recommendations for utilization of facial recognition 
technologies to various U.S. Government and law enforcement agencies, 
including the Federal Bureau of Investigation (FBI), Office of 
Biometric Identity Management (OBIM) at the Department of Homeland 
Security (DHS), Department of Homeland Security Science and Technology 
Directorate (DHS S&T), the Department of Homeland Security's U.S. 
Customs and Border Protection agency (DHS CBP), and the Intelligence 
Advanced Research Projects Activity (IARPA) at the office of the 
Director of National Intelligence.
    Historically and currently, NIST biometrics research has assisted 
DHS. For example, NIST's research was used by DHS in its transition to 
ten prints for the former US-VISIT program. NIST is currently 
collaborating with DHS OBIM on face image quality standards. 
Additionally, NIST is working with DHS CBP to analyze performance 
impacts due to image quality and traveler demographics and provide 
recommendations regarding match algorithms, optimal thresholds and 
match gallery creation for its Traveler Verification Service (TVS).
              nist face recognition vendor testing program
    NIST's Face Recognition Vendor Testing Program (FRVT) was 
established in 2000 to provide independent evaluations of both 
prototype and commercially-available facial recognition algorithms. 
These evaluations provide the U.S. Government with information to 
assist in determining where and how facial recognition technology can 
best be deployed. FRVT results also help identify future research 
directions for the facial recognition community.
    The 2013 FRVT tested facial recognition algorithms submitted by 16 
organizations, and showed significant algorithm improvement since 
NIST's 2010 FRVT test. NIST defined performance by recognition 
accuracy--how many times the software correctly identified the photo--
and the time the algorithms took to match one photo against large photo 
data sets.
    The 2018 FRVT tested 127 facial recognition algorithms from the 
research laboratories of 39 commercial developers and one university, 
using 26 million mugshot images of 12 million individuals provided by 
the FBI. The 2018 FRVT measured the accuracy and speed of one-to-many 
facial recognition identification algorithms. The evaluation also 
contrasted mugshot accuracy with that from lower quality images. The 
findings, reported in NIST Interagency Report 8238,\3\ showed that 
massive gains in accuracy have been achieved since the FRVT in 2013, 
which far exceed improvements made in the prior period (2010-2013). The 
accuracy gains observed in the 2018 FVRT study stem from the 
integration, or complete replacement, of older facial recognition 
techniques with those based on deep convolutional neural networks. 
While the industry gains are broad, there remains a wide range of 
capabilities, with some developers providing much more accurate 
algorithms than others. Using FBI mugshots, the most accurate 
algorithms fail only in about \1/4\ of 1 percent of searches, and these 
failures are associated with images of injured persons and those with 
long time lapse since the first photograph. The success of mugshot 
searches stems from the new generation of facial recognition 
algorithms, and from the adoption of portrait photography standards 
first developed at NIST in the late 1990's.
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    \3\ https://nvlpubs.nist.gov/nistpubs/ir/2018/NIST.IR.8238.pdf.
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    The 2019 FRVT quantified the accuracy of face recognition 
algorithms for demographic groups defined by sex, age, and race or 
country of birth, for both one-to-one verification algorithms and one-
to-many identification search algorithms. NIST conducted tests to 
quantify demographic differences for 189 face recognition algorithms 
from 99 developers, using 4 collections of photographs with 18.27 
million images of 8.49 million people. These images came from 
operational databases provided by the State Department, the Department 
of Homeland Security, and the FBI. Previous FRVT reports \4\ documented 
the accuracy of these algorithms and showed a wide range in accuracy 
across algorithms. The more accurate algorithms produce fewer errors 
and can therefore be anticipated to have smaller demographic 
differentials.
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    \4\ Part 1: https://www.nist.gov/system/files/documents/2019/11/20/
frvt_report_2019- _11_19_0.pdf and Part 2: https://www.nist.gov/system/
files/documents/2019/09/11/ni- stir_8271_20190911.pdf.
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    NIST Interagency Report 8280,\5\ released on December 19, 2019, 
quantifies the effect of age, race, and sex on face recognition 
performance. It found empirical evidence for the existence of 
demographic differentials in face recognition algorithms that NIST 
evaluated. The report distinguishes between false positive and false 
negative errors, and notes that the impacts of errors are application 
dependent.
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    \5\ https://nvlpubs.nist.gov/nistpubs/ir/2019/NIST.IR.8280.pdf.
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    I will first address one-to-one verification applications. There, 
false positive differentials are much larger than for false negatives 
and exist across many, but not all, algorithms tested. Across 
demographics, false positives rates often vary by factors of 10 to 
beyond 100 times. False negatives tend to be more algorithm-specific, 
and often vary by factors below 3. False positives might present a 
security concern to the system owner, as they may allow access to 
impostors. False positives may also present privacy and civil rights 
and civil liberties concerns such as when matches result in additional 
questioning, surveillance, errors in benefit adjudication, or loss of 
liberty. False positives are higher in women than in men and are higher 
in the elderly and the young compared to middle-aged adults. Regarding 
race, we measured higher false positive rates in Asian and African 
American faces relative to those of Caucasians. There are also higher 
false positive rates in Native American, American Indian, Alaskan 
Indian, and Pacific Islanders. These effects apply to most algorithms, 
including those developed in Europe and the United States. However, a 
notable exception was for some algorithms developed in Asian countries. 
There was no such dramatic difference in false positives in one-to-one 
matching between Asian and Caucasian faces for algorithms developed in 
Asia. While the NIST study did not explore the relationship between 
cause and effect, one possible connection, and area for research, is 
the relationship between an algorithm's performance and the data used 
to train the algorithm itself.
    I will now comment on one-to-many search algorithms. Again, the 
impact of errors is application-dependent. False positives in one-to-
many search are particularly important because the consequences could 
include false accusations. For most algorithms, the NIST study measured 
higher false positives rates in women, African Americans, and 
particularly in African American women. However, the study found that 
some one-to-many algorithms gave similar false positive rates across 
these specific demographics. Some of the most accurate algorithms fell 
into this group. This last point underscores one overall message of the 
report: Different algorithms perform differently. Indeed all of our 
FRVT reports note wide variations in recognition accuracy across 
algorithms, and an important result from the demographics study is that 
demographic effects are smaller with more accurate algorithms.
    A general takeaway from these studies is that, there is significant 
variance between the performance facial recognition algorithms, that 
is, some produce significantly fewer errors than others. Consequently, 
users, policy makers, and the public should not think of facial 
recognition as either always accurate or always error prone.
                 nist face in video evaluation program
    The Face in Video Evaluation Program (FIVE) assessed the capability 
of facial recognition algorithms to correctly identify or ignore 
persons appearing in video sequences. The outcomes of FIVE are 
documented in NIST Interagency report 8173,\6\ which enumerates 
accuracy and speed of facial recognition algorithms applied to the 
identification of persons appearing in video sequences drawn from 6 
different video datasets. NIST completed this program in 2017.
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    \6\ https://www.nist.gov/publications/face-video-evaluation-five-
face-recognition-non-cooperative-subjects.
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                human factors: facial forensic examiners
    NIST is researching how to measure the accuracy of forensic 
examiners matching identity across different photographs. The study 
measures face identification accuracy for an international group of 
professional forensic facial examiners working under circumstances 
approximating real-world casework. The findings, published in the 
proceedings of the National Academy of Sciences,\7\ showed that 
examiners and other human face ``specialists,'' including forensically-
trained facial reviewers and untrained super-recognizers, were more 
accurate than the control groups on a challenging test of face 
identification. It also presented data comparing state-of-the-art 
facial recognition algorithms with the best human face identifiers. The 
best machine performed in the range of the best-performing humans, who 
were professional facial examiners. However, optimal face 
identification was achieved only when humans and machines collaborated.
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    \7\ https://www.pnas.org/content/115/24/6171.
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                     voluntary consensus standards
    When properly conducted, standards development can increase 
productivity and efficiency in Government and industry, expand 
innovation and competition, broaden opportunities for international 
trade, conserve resources, provide consumer benefit and choice, improve 
the environment, and promote health and safety.
    In the United States, most standards development organizations are 
industry-led private-sector organizations. Many voluntary consensus 
standards from those standard development organizations are appropriate 
or adaptable for the Government's purposes. OMB Circular A-119 directs 
the use of such standards by U.S. Government agencies, whenever 
practicable and appropriate, to achieve the following goals:
   eliminating the cost to the Federal Government of developing 
        its own standards and decreasing the cost of goods procured and 
        the burden of complying with agency regulation;
   providing incentives and opportunities to establish 
        standards that serve National needs, encouraging long-term 
        growth for U.S. enterprises and promoting efficiency, economic 
        competition, and trade; and
   furthering the reliance upon private-sector expertise to 
        supply the Federal Government with cost-efficient goods and 
        services.
      examples of nist consensus standards development activities
    ANSI/NIST-ITL.--The ANSI/NIST-ITL standard for biometric 
information is used in 160 countries to ensure biometric data exchange 
across jurisdictional line and between dissimilar systems. One of the 
important effects of NIST work on this standard is that it allows 
accurate and interoperable exchange of biometrics information by law 
enforcement globally and enables them to identify criminals and 
terrorists. NIST's own Information Technology Laboratory is an American 
National Standards Institute (ANSI)-accredited standard development 
organization. Under accreditation by ANSI, the private-sector U.S. 
standards federation, NIST continues to develop consensus biometric 
data interchange standards. Starting in 1986, NIST has developed and 
approved a succession of data format standards for the interchange of 
biometric data. The current version of this standard is ANSI/NIST-ITL 
1: 2015, Data Format for the Interchange of Fingerprint, Facial & Other 
Biometric Information.\8\ This standard continues to evolve to support 
Government applications including law enforcement, homeland security, 
as well as other identity management applications. Virtually all law 
enforcement biometric collections world-wide use the ANSI/NIST-ITL 
standard. NIST biometric technology evaluations in fingerprint, face, 
and iris have provided the Government with timely analysis of market 
capabilities to guide biometric technology procurements and 
deployments.
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    \8\ https://www.nist.gov/publications/data-format-interchange-
fingerprint-facial-other-biometric-information-ansinist-itl-1-1.
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   iso/iec joint technical committee 1, subcommittee 37 (jtc1/sc37)--
                               biometrics
    From the inception of the ISO Subcommittee on Biometrics in 2002, 
NIST has led and provided technical expertise to develop international 
biometric standards in this subcommittee. Standards developed by the 
Subcommittee on Biometrics have received wide-spread international and 
National market acceptance. Large international organizations, such as 
the ICAO for Machine-Readable Travel Documents and the International 
Labour Office (ILO) of the United Nations for the verification and 
identification of seafarers, specify in their requirements the use of 
some of the international biometric standards developed by this 
subcommittee.
    Since 2006, JTC1/SC37 has published a series of standards on 
biometric performance testing and reporting, many of which are based on 
NIST technical contributions. These documents provide guidance on the 
principles and framework, testing methodologies, modality-specific 
testing, interoperability performance testing, access control 
scenarios, and testing of on-card comparison algorithms for biometric 
performance testing and reporting. NIST contributes toward the 
development of these documents and follows their guidance and metrics 
in its evaluations, such as the FRVT.
                               conclusion
    NIST is proud of the positive impact it has had in the last 60 
years on the evolution of biometrics capabilities. With NIST's 
extensive experience and broad expertise, both in its laboratories and 
in successful collaborations with the private sector and other 
Government agencies, NIST is actively pursuing the standards and 
measurement research necessary to deploy interoperable, secure, 
reliable, and usable identity management systems.
    Thank you for the opportunity to testify on NIST's activities in 
facial recognition and identity management. I would be happy to answer 
any questions that you may have.

    Chairman Thompson. Thank you very much.
    I thank all of the witnesses for their testimony.
    I remind each Member that he or she will have 5 minutes to 
question the panel.
    I will now recognize myself for questions.
    Dr. Romine, we will start off with you. Part of your NIST 
report was like next generation technology, as I understand, 
that CBP will use or did you review existing technology?
    Mr. Romine. We are not certain of that. We certainly intend 
to continue our investigations. The existence of the specific 
algorithms that we test, those algorithms are submitted to us 
by the vendors. We have no independent way to correlate whether 
those are the identical algorithms that are being used in the 
field.
    Chairman Thompson. So part of what you said is how the 
technology is deployed depends on the application of the 
technology. Explain that a little more to the committee.
    Mr. Romine. Certainly. Our approach is that the significant 
thing to be cognizant of is the risk associated with the 
deployment, and the studies that we do help to inform policy 
makers, such as Members of Congress, as well as operators of 
these technologies, about how to quantify those risks at least 
for the algorithms themselves.
    The deployed systems have other characteristics associated 
with them that we do not test. We test only the algorithms 
currently.
    The second point is that that risk that comes from the 
error rates associated with the algorithms is part of a much 
larger risk management that the operators have to undertake.
    For example, access to critical infrastructures and access 
control systems to critical infrastructures is different than 
access to a phone that you might have. The risks are different 
in those cases.
    Chairman Thompson. Thank you.
    Mr. Wagner, can you share with the committee the extent 
that CBP goes to to protect the information collected in this 
process?
    Mr. Wagner. Sure. So the photographs that are taken by one 
of our stakeholders' cameras, they are encrypted. They are 
transmitted securely to the CBP cloud infrastructure where the 
gallery is positioned.
    Chairman Thompson. Right.
    Mr. Wagner. The pictures are templatized, which means they 
are turned into some type of mathematical structure that cannot 
be reverse-engineered, and they are matched up with the 
templatized photos that we have pre-staged in the gallery, and 
then just a response goes back, yes or no, with a unique 
identifier.
    Chairman Thompson. Thank you.
    So the comment that 2 to 3 percent of people who are 
misidentified, what is CBP doing to try to get that to zero?
    Mr. Wagner. Right. So it is not that they are 
misidentified. It just means we did not match them to a picture 
in the gallery that we did have of them. So we should have 
matched them.
    You are right. That should be at zero, and that is where we 
look at the operational variables, the camera, the picture 
quality, the human behaviors when the photo was taken, the 
lighting, those different types, and then the age of the photo.
    Then what we have seen in the NIST report, your gallery 
size impacts your match rate. I think NIST tested galleries up 
to 12 million size. We are comparing against a few thousand 
here at most.
    Then the numbers of photos that we have of the particular 
individual can impact which one we match against and then some 
of your match rates, and then the age of the photo. So if you 
had your passport taken at age 20 and you are now 29 and your 
face has changed in the dimensions, we are going to struggle to 
match against that, which is then compounded by poor lighting 
conditions or the person moving when the photo is taken or a 
poorer-quality photo.
    Chairman Thompson. Well, Mr. Mina, listening to what you 
just heard, have you all dealt with any complaints from 
citizens about this technology?
    Mr. Mina. Mr. Chairman, we have received one complaint that 
referenced this facial recognition technology. However, we have 
not seen a trend, and that is when we would actually, in fact, 
open an investigation in this matter.
    We are working, as I mentioned, on the policy side of the 
house advising CBP directly.
    The other way in which we also hear from the community, as 
you may know, is through our Community Engagement Roundtables 
around the country, and we have heard concerns in those forums 
about facial recognition technology, and those are concerns 
that we are using to inform our advice.
    Chairman Thompson. So can you provide the committee with 
where you have held those forums around the country?
    Mr. Mina. Yes, absolutely.
    We do roundtables in about 18 cities, and not to say that 
these concerns have been raised in every single location, but 
certainly in some.
    Then, again, we will continue to have those discussions 
with CBP and with S&T in the future at future roundtables.
    Chairman Thompson. Thank you.
    Last, Mr. Wagner, I am not sure you have information on 
this, but last month Iranian and Lebanese nationals and 
individuals who travel to Iran and Lebanon, most of whom were 
U.S. citizens or green card holders, were targeted, detained, 
and subjected to prolonged questioning of up to 12 hours at the 
Blaine area port of entry. I understand an internal CBP memo 
indicates people were also questioned based on their religion, 
which is completely unacceptable.
    I understand CBP has admitted to enormous mistakes in this 
incident. If you know, how did this situation happen?
    What is CBP doing to ensure that it never happens again?
    Mr. Wagner. So there was no National directive or guidance 
that went out other than because of the things taking place in 
Iran, the concerns about retaliation, we put our field managers 
on alert to be more vigilant about current events that are 
happening and work with your State, local, and Federal 
counterparts and, you know, really just be vigilant.
    There was some more prescriptive guidance that went out at 
the local level in Blaine, Washington, which we are reviewing 
right now because there are a lot of concerning things, I 
think, that we saw in the interpretation of that guidance and 
the management oversight as that weekend was unfolding and 
people were being referred in for additional inspections and 
questioning, and there are some concerning points about the 
management engagement or lack thereof of what transpired.
    So there is an internal investigation that CBP is 
conducting. Civil Rights and Civil Liberties is conducting an 
investigation, and when we get the results of that, we will 
then proceed, you know, accordingly, depending on what those 
results say.
    Chairman Thompson. Mr. Mina, were you aware of that?
    Mr. Mina. Yes, and as Mr. Wagner said, we do have an open 
investigation in this matter.
    Chairman Thompson. OK. Thank you.
    Ranking Member, are you ready?
    Mr. Rogers. I am ready. Thank you.
    Chairman Thompson. I yield to the Ranking Member for an 
opening statement.
    Mr. Rogers. I am sorry for being late. We just got back 
from the National Prayer Breakfast.
    Thank you, Mr. Chairman.
    After the tragic events of September 11, Congress 
recognized that biometric systems are essential to our homeland 
security. Following the recommendation of the 9/11 Commission, 
Congress charged DHS with the creation of an automated, 
biometric entry and exit system.
    Customs and Border Protection and the Transportation and 
Security Administration have already demonstrated the 
capability of biometrics to improve security, facilitate 
travel, and better enforced existing immigration law.
    Government and the private sector have made enormous 
strides in the accuracy, speed, and deployment of biometric 
systems. Biometric technologies of all types have seen 
improvements.
    These advances in facial recognition algorithms, in 
particular, are transformational. The National Institute of 
Standards and Technology is the leader in testing and 
evaluation for biometric technologies.
    Dr. Romine and his team have done incredible work to help 
Congress, DHS, and industry understands the capability of 
currently available algorithms, but I am concerned that some of 
my colleagues have already jumped to the misleading conclusion 
that NIST reports on facial recognition.
    Just hours after NIST released the 1,200 pages of technical 
data, the Majority tweeted that this report shows facial 
recognition is even more unreliable and racially biased than we 
feared. If the Majority had taken the time to read the full 
report before tweeting, they would have found that the real 
headline, NIST determined that facial recognition algorithms 
being adopted by DHS has no statistically detectable race or 
gender bias.
    In other words, NIST could find no statistical evidence 
that facial recognition algorithms that DNS is adopting 
contains racial bias.
    I hope my colleagues will listen to Dr. Romine as he 
explains how the NIST report proves that race or gender bias is 
statistically undetectable in the most accurate algorithms.
    The reality is that facial recognition technologies can 
improve existing processes by reducing human error. These 
technologies are tools that cannot and will not replace the 
final judgment of CBP or TSA officers.
    Concerns regarding privacy and civil rights are well-
intentioned, but these concerns can be fully addressed in how 
biometric systems are implemented by DHS.
    I look forward to hearing the steps that CRCL is taking to 
coordinate with CBP and to protect privacy and civil rights of 
Americans.
    But as I have said before, halting all Government biometric 
programs is not a solution. Doing so ignores the critical facts 
that the technology that DHS uses is not racially biased. It 
does not violate the civil rights of Americans. It is accurate. 
Most importantly, it does protect the homeland.
    I appreciate the Chairman calling the hearing today. It is 
important for Congress to further educate itself on this issue. 
I look forward to getting the facts, and I yield back, Mr. 
Chairman.
    [The statement of Ranking Member Rogers follows:]
                Statement of Ranking Member Mike Rogers
                            February 6, 2020
    After the tragic events of September 11, Congress recognized that 
biometric systems are essential to our homeland security.
    Following the recommendation of the 9/11 Commission, Congress 
charged DHS with the creation of an automated biometric entry and exit 
system.
    Customs and Border Protection and the Transportation Security 
Administration have already demonstrated the capability of biometrics 
to improve security, facilitate travel, and better enforce existing 
immigration laws.
    Government and the private sector have made enormous strides in the 
accuracy, speed, and deployment of biometrics systems.
    Biometric technologies of all types have seen improvements.
    The advances in facial recognition algorithms in particular are 
transformational.
    The National Institute of Standards and Technology is the leader in 
testing and evaluation for biometric technologies.
    Dr. Romine and his team have done incredible work to help Congress, 
DHS, and industry understand the capability of currently-available 
algorithms.
    But I'm concerned that some of my colleagues have already jumped to 
misleading conclusions regarding the NIST report on facial recognition.
    Just hours after NIST released over 1,200 pages of technical data, 
the Majority tweeted ``This report shows facial recognition is even 
more unreliable and racially biased than we feared . . . [''.
    If the Majority had taken the time to read the full report before 
tweeting, they would have found the real headline: NIST determined that 
the facial recognition algorithm being adopted by DHS had no 
statistically detectable race or gender bias.
    In other words, NIST could find NO statistical evidence that the 
facial recognition algorithm DHS is adopting contains racial bias.
    NIST found measurable and significant errors and bias in OTHER 
facial recognition algorithms, but NOT in the algorithm used by DHS.
    I hope that my colleagues will listen when Dr. Romine explains how 
the NIST report proves that race or gender bias is statistically 
undetectable in the most accurate algorithms.
    The reality is that facial recognition technologies can improve 
existing processes by reducing human error.
    These technologies are tools that cannot and will not replace the 
final judgment of CBP or TSA officers.
    Concerns regarding privacy and civil rights are well-intentioned.
    But these concerns can be fully addressed in how biometric systems 
are implemented by DHS.
    I look forward to hearing the steps CRCL is taking to coordinate 
with CBP and protect the privacy and civil rights of Americans.
    But as I have said before, halting all Government biometric 
programs is not the solution.
    Doing so ignores these critical facts: The technology DHS uses is 
NOT racially biased; It does NOT violate the civil rights of Americans; 
It IS accurate; and most importantly, it DOES protect the homeland.
    I appreciate the Chairman calling this hearing today. It's 
important for Congress to further educate itself on this issue. I look 
forward to getting the facts on the record.

    Chairman Thompson. Thank you very much.
    I wish you had heard the testimony because there was some 
testimony we heard to the contrary.
    Mr. Rogers. I look forward to probing them on that.
    Chairman Thompson. All right. Well, I recognize the 
gentleman for his questions.
    Mr. Rogers. My statement is wrong, to get to the Chairman's 
point. Anybody can jump at it.
    Mr. Wagner. I would never tell Congress they are wrong.
    Mr. Rogers. You are one of the few people who will not do 
that.
    [Laughter.]
    Mr. Rogers. Literally, I mean, my understanding is there is 
no statistical evidence that there is racial bias. Is that an 
inaccurate statement?
    Mr. Romine. Thank you for the question.
    In the highest-performing algorithms for one-to-many 
matches, the highest-performing algorithms we saw undetectable 
bias. The demographic differentials that we were measuring we 
say are undetectable in the report.
    Mr. Rogers. So what do you mean by undetectable?
    Mr. Romine. What I mean by that is that in the testing that 
we undertook, there was no way to determine--let me back up and 
say the idea of having absolutely zero false positives is a big 
challenge.
    Mr. Rogers. Did you test the NEC-3 algorithm being used by 
DHS?
    Mr. Romine. We tested algorithms from NEC. We have no 
independent way to verify that that is the specific algorithm 
that is being used by CBP. That would be something that CBP and 
NEC would have to attest to.
    From our perspective, the vendor provides us algorithms. 
They are black boxes that we test just the performance of the 
algorithm that is submitted to us by the vendor.
    Mr. Rogers. Mr. Wagner, is CBP currently working to 
implement NEC-3 algorithms?
    Mr. Wagner. Any what? I am sorry.
    Mr. Rogers. NEC-3 algorithms.
    Mr. Wagner. Yes, we are using an earlier version of NEC 
right now, and I believe we are testing NEC-3, which is the 
version that was tested, and the plan is to use it next month 
in March to switch or to upgrade basically to that one.
    Mr. Rogers. OK. Dr. Romine, who can participate in the 
facial recognition vendor test? Is it accurate to say that some 
algorithms are far less accurate and sophisticated than others?
    Mr. Romine. Yes, sir, that is correct. Anyone around the 
globe can participate. We have participants from industries, 
biometrics industries around the country, but also from 
universities and some experimental systems as well.
    Mr. Rogers. Great. That is all I have, Mr. Chairman. Thank 
you.
    Chairman Thompson. Thank you very much.
    Mr. Wagner, let's get clear. The C-3, you do not have it 
operational anywhere in the country, right? You are testing it.
    That technology goes into being, you said, next month?
    Mr. Wagner. The NEC-3 algorithm we are planning to 
implement next month. The earlier version of it is operational 
now.
    Chairman Thompson. But the one we are talking about is not?
    Mr. Wagner. Correct.
    Chairman Thompson. Dr. Romine, let's be clear. You 
mentioned that African Americans and Asians get misidentified.
    Mr. Romine. In the highest-performing algorithms we do not 
see that to a statistical level of significance, for one-to-
many algorithms, the identification algorithms.
    For the verification algorithms, we do see or the one-to-
one algorithms we do see evidence of demographic effects for 
African Americans, for Asians, and others.
    Chairman Thompson. Thank you.
    The Chair recognizes Ms. Slotkin for 5 minutes.
    Ms. Slotkin. Thank you.
    Thank you for clarifying that because it was hard to 
understand from your testimony.
    So just to be clear, Dr.--I am sorry. Can you pronounce 
your name? I want to pronounce it right.
    Mr. Romine. Romine.
    Ms. Slotkin. Romine. Sorry. Apologies.
    Mr. Romine. That is quite all right.
    Ms. Slotkin. So in a certain segment of these algorithms, 
there is some evidence that they have higher rates of mistakes 
for African Americans and Asian Americans; is that correct?
    Mr. Romine. It is correct that most of the algorithms in 
the one-to-many that are submitted do exhibit those 
differentials. The highest performing ones in the one-to-many 
do not.
    Ms. Slotkin. OK. So some do and some do not. I am just 
trying to clarify.
    Thank you all for being here. I am from Michigan. So we 
have a long history of needing our CBP officers to protect us 
at the Detroit airport and all of our bridges and crossings. So 
can you help me understand?
    Is this technology, Mr. Wagner, being used in any way at 
our bridge crossings in the Northern Border?
    Mr. Wagner. No, not at the bridge crossings.
    Ms. Slotkin. OK. But at the airport.
    Mr. Wagner. At the airport.
    Ms. Slotkin. I know at the airport.
    So while I recognize it seems to be a small number of times 
or of these programs where they have detected more problems 
with particularly African American women I think were mentioned 
and Asian Americans, walk me through the process where it would 
be you are an average citizen. You are from my district. You 
are an African American woman.
    Let's say we employ this technology and it shows a 
positive, right, a link. Just walk me through that process and 
how you would deal with that at the actual border for that 
actual citizen.
    Mr. Wagner. You would then just show your passport, which 
is what you do today, and a person would manually review it if 
you did not match.
    Ms. Slotkin. If they showed the passport but the technology 
still showed a match, what does that officer do in that 
situation, if the machine is saying one thing and the passport 
is saying another?
    Mr. Wagner. We would go on the basis of the document we are 
presenting and which photograph we have identified you with or 
which identity we have identified you with.
    Ms. Slotkin. OK. Then that person would cross the border 
and go on with their--I am just asking.
    Mr. Wagner. Yes.
    Ms. Slotkin. For the average person to understand how this 
is being implemented.
    Mr. Wagner. What we are matching people against, U.S. 
citizens, is that passport photo. We have an electronic copy of 
that passport database. So----
    Chairman Thompson. Excuse me just a minute.
    Staff, you all are being most disrespectful to the hearing.
    Please.
    Mr. Wagner. So when you are flying into the country, you 
preassemble a gallery of those photographs, and that is what we 
match you against. So on the officer's screen, they will see 
the photograph which should be also what is printed on your 
passport, which also should be on that electronic chip in your 
passport.
    We will look at you and make sure you are all that same 
person. If it does not match against that, then we will have to 
figure out why.
    Ms. Slotkin. When you figure out why, is that individual 
allowed to progress?
    You know, we got to Windsor to like see a concert, and we 
go to Canada quite often in Michigan.
    Mr. Wagner. Right. It could be as simple as just looking at 
your passport document and saying, ``OK. That's you.''
    Ms. Slotkin. OK.
    Mr. Wagner. Then we will figure out later what happened.
    Ms. Slotkin. Then what happens with that data, right?
    So let's say a woman has gone to her concert in Canada. 
What happens to her data where it is flagged that she is 
falsely flagged that she is matched against someone who has 
done something wrong?
    What happens in the Department to that information?
    Mr. Wagner. If you are a U.S. citizen, the new photograph 
we take is discarded after 12 hours. There is no need for us to 
keep the new photograph.
    There is a record of the transaction that you crossed the 
border. If there is some type of error in that, then our 
analysts would look at it and correct it basically.
    If you have matched, which happens very often in a 
biographical sense, your name, date of birth, to the wrong 
person even though your biographic match is identical to 
someone else, that is where we can also use the facial 
recognition to help us distinguish between the people with the 
common names.
    We can put notes in the system then to advise the officers 
to suppress that information from even appearing when we query 
your passport the next time.
    Ms. Slotkin. Tell me how this technology where you have 
been implementing it at different airports and different land 
borders, I understand, in the South. Tell me: What are the 
results?
    How many people have you identified in a positive way that 
needed to be identified?
    Tell me some statistics to help me to demonstrate the value 
of these programs.
    Mr. Wagner. Sure. It is 43.7 million people we have run 
through it to date at all the different locations, inbound, 
outbound, cruise ships, land border pedestrians. We have caught 
252 imposters, people with legitimate travel documents 
belonging to someone else. I think 75 of those were U.S. travel 
documents.
    Remember for U.S. travel documents the only biometric we 
have is that digitized photo that the State Department has put 
on the electronic chip. There is no fingerprint record. There 
is no fingerprint requirement to get a U.S. passport.
    I am not advocating for one, but there is not one there. So 
the only biometric we have on a U.S. travel document is that 
digitized photograph, and that is a worldwide standard.
    That chip is allowed to be opened by any country 
participating in that ICAO scheme that can access the chip and 
pull off the digital photograph and then do some type of 
comparison to that.
    Ms. Slotkin. So in my remaining time, so tens of millions 
of people that you have used that have gone through this 
technology, just tell me a little bit more about your stats. 
How many positive stories? How many negative hits?
    Mr. Wagner. So our match rate is about 97 to 98 percent. 
That 2 to 3 percent generally means we could not find that 
person in that preassembled gallery, meaning we did not match 
against anything. We did not match against the wrong person. We 
just did not find a match of people traveling.
    It could be various environmental or operational reasons 
for why that happened.
    Ms. Slotkin. How many are false positives?
    Mr. Wagner. I am not aware of any.
    Ms. Slotkin. OK.
    Mr. Wagner. But there may be a small handful. I am just not 
aware of any, but as we built this and tested it, we are just 
not seeing that.
    Ms. Slotkin. I think my time has expired. Thank you, 
gentlemen.
    Chairman Thompson. Thank you.
    The Chair recognizes the gentleman from Texas, Mr. McCaul.
    Mr. McCaul. Thank you, Mr. Chairman.
    You know, the 9/11 Commission recommended the use of 
biometrics for those entering and leaving the United States, 
and I believe that technology is our friend in stopping 
terrorists and bad actors from entering this country.
    We have seen it time and time again, and my first question 
is my understanding is the entry/exit program, American 
citizens can opt out of that program. Is that correct?
    Mr. Wagner. Yes, that is correct.
    Mr. McCaul. So there is no requirement that all Americans 
have to be subjected to this.
    Mr. Wagner. No, but people have to like establish their 
identity.
    Mr. McCaul. Yes.
    Mr. Wagner. Once we determine either through manual review 
of the passport or by using the technology, they are a U.S. 
citizen and they are excluded from the biometric tracking 
requirement.
    Mr. McCaul. Right.
    Mr. Wagner. But they can opt out of having their picture 
taken to make that determination.
    Mr. McCaul. It is just like we use with global entry. Most 
of my constituents love global entry. You know, I got the CLEAR 
Program, as did Mr. Katko, associated with TSA so you could put 
your fingerprints down and get to the head of the TSA PreCheck 
line.
    These are the technologies. I think it made it easier for 
the traveling public, but also the great thing is it does not 
lie. Biometrics, it is you, and it is hard to fake that.
    The last Congress we passed out of the committee my bill, 
the Biometric Identification Transnational Migration Alert 
Program, otherwise known as BITMAP. Now, I know this is an ICE 
program, not CBP, but it passed overwhelmingly in a bipartisan 
way on the floor, 272 to 119.
    It reauthorizes successful programs started under the Obama 
administration that Secretary Jeh Johnson and I talked a great 
deal about.
    How can we use BITMAP to identify when these people were 
coming into our hemisphere?
    They may change their names multiple times along the route 
to get to the United States, yet their facial recognition, 
their biometrics do not. Their names do, but not their 
biometrics.
    This has been, in my judgment, a very successful program in 
keeping terrorists, human traffickers, and bad actors out of 
this country.
    In fact, this program has enrolled over 155,000 encounters 
of persons of interest and 460 known and suspected terrorists, 
including arresting violent criminals and rapists involved in 
transnational criminal organizations.
    So, again, Mr. Wagner, can you comment on why that program 
is so valuable to the security of the United States and the 
American people?
    Mr. Wagner. Sure. It is critically important because, as 
you mentioned, people do change their biographic details, and 
you know, most of our watch list searches are biographically-
based.
    But if we can identify people, especially people traveling 
via air, that we have National security concerns about and they 
are entering our hemisphere, if they are entering in Central or 
South America, we can work with our partners down there and 
establish on a biometric basis who that person is so that no 
matter what identity they show up in later, if they show up on 
the U.S.-Mexico border, we can run the biometric confirmation 
to see, well, who were they when they first flew into the 
hemisphere. It is critically important.
    Mr. McCaul. The travel documents can change and passports 
are stolen and manufactured.
    Mr. Wagner. Absolutely.
    Mr. McCaul. That is not accurate, but the biometrics do not 
lie.
    Mr. Wagner. Correct. People change documents, steal 
documents, borrow documents, purchase documents.
    It is harder to alter them now, but the ability to get a 
legitimate document that looks like you, and if you can pass by 
the visual inspection of somebody glancing at the little 22 
photograph on it, yes, yes, and that is where the risk is.
    Mr. McCaul. It is unfortunate the Senate in its usual 
wisdom did not pass this bill. They stole a lot of legislation 
the Chairman and I in a bipartisan way passed last Congress, 
and that is unfortunate. I would hope we could pass this bill 
again this Congress.
    I do think we have to look at civil liberties and privacy 
as well, but I do think entry/exit is opt-out. It applies 
primarily to Americans who would want to opt in and foreign 
nationals, and BITMAP applies really almost really to foreign 
nationals themselves.
    So I want to thank the witnesses for your testimony.
    Mr. Chairman, thanks for having this hearing. I yield back.
    Chairman Thompson. Thank you very much.
    The Chair recognizes the gentlelady from New York, Ms. 
Clarke.
    Ms. Clarke. Thank you very much, Mr. Chairman. I thank our 
Ranking Member. I thank our expert witnesses who testified 
before us today.
    But it is time that we face the fact. Unregulated, facial 
recognition is just not an option. We can debate and disagree 
about the exact situations where we should permit the use of 
facial recognition, but we should all agree that there is no 
situation where facial recognition should be used without 
safeguards against bias and protections for privacy.
    Right now in terms of regulation, facial recognition is 
still in the Wild West. Meanwhile facial recognition 
technologies are routinely misidentifying women and people of 
color.
    Although there are some promising applications for facial 
recognition, these benefits do not outweigh the risk of 
automating discrimination.
    We have seen what happens when technology is widely 
deployed before Congress can impose meaningful safeguards. So 
let us all look before we leap.
    Mr. Wagner, some of our staff have observed issues with 
facial recognition technology screening at airports. For 
example, we have seen passengers, and particularly darker-
skinned passengers it seems, not able to be matched due to poor 
lighting or other factors.
    Does CBP track how often its systems failed to capture 
photos of sufficient quality for matching?
    Mr. Wagner. We track the number of--well, we do not own all 
of the cameras. So it is difficult for us to track what an 
airline does or how many pictures they might be taking before 
they submit one to us for matching. Because in the departure 
environment, the airports or the airlines are the ones that own 
them.
    So we are tracking how many pictures we receive and what 
our match rates against them are.
    Ms. Clarke. Yes, I was just wondering about the quality 
because if the photo quality is not good enough, the accuracy 
of the matching algorithm is irrelevant.
    Mr. Wagner. Absolutely. So we set a minimum standard. The 
picture has to be of this quality before it even----
    Ms. Clarke. But do you track the numbers of photos that do 
not meet your standard?
    You said you have all of these other partners that are 
taking photos.
    Mr. Wagner. Right.
    Ms. Clarke. If you are using their material, then now you 
are dealing with something that has become irrelevant if you do 
not know what subset of those do not meet your quality control, 
right?
    Mr. Wagner. Well, we know the pictures that they transmit 
to us, whether or not they meet----
    Ms. Clarke. Right. But have you----
    Mr. Wagner. But we do not know how many attempts they made.
    Ms. Clarke. Absolutely, and you do not know the quality. 
You do not know how much of that, what percentage of that does 
not meet your standard.
    Mr. Wagner. Well, we look at the number of passengers on--
--
    Ms. Clarke. Do you know the percentage that does not meet 
your standard?
    Mr. Wagner. Not offhand, no.
    Ms. Clarke. OK. How does CBP plan to address these issues 
to ensure it can capture high-quality images of travelers for 
successful facial recognition screening?
    Mr. Wagner. That is the partnership with NIST where we look 
at. We have a high-performing algorithm. Now we look at the 
operational variables to make that even more high-performing.
    Ms. Clarke. So what I would say to you is that then until 
you have met that standard, you are not doing the public a 
service.
    Mr. Wagner. What standard is that?
    Ms. Clarke. Of quality control.
    Mr. Wagner. We are developing that standard.
    Ms. Clarke. Right. It is not developed, right? You're 
developing.
    Mr. Wagner. Not necessarily, no. I mean, what we are 
seeing, if we are matching it at 97 to 98 percent rate, we are 
getting----
    Ms. Clarke. Let me go on to another question.
    When you are in that 3 percent, it does not matter about 
the other----
    Mr. Wagner. We are not seeing demographic-based, you know, 
rates in that 3 percent, and that is when we partnered with 
NIST to come in and help us understand that better to be sure 
that that is the case.
    Ms. Clarke. Very well. Very well.
    I understand that since our last hearing CBP completed 
operational testing of the biometric entry/exit systems at 
airports. The results indicated that the system accurately 
matched images when captured, but the rate of successfully 
capturing an image was significantly lower than expected, 80 
percent compared to 97 percent.
    Most of these issues were attributed to airlines reverting 
to manually processing passengers to speed the boarding 
process. Are you aware of these findings?
    Mr. Wagner. Yes, and that is as we were developing the 
operational variables to look at, No. 1, does it even work, 
right? Can we make it work?
    Now we look at and we work with the airlines to not shut 
down their boarding. What is the ease of the application of the 
traveler engaging with that?
    Ms. Clarke. So quickly, what steps is CBP taking to work 
with airlines to increase image capture rates?
    Mr. Wagner. So one is publishing the regulation, which 
would then put the requirement onto the foreign national who 
has to comply with the biometric exit Congressional mandate.
    Then we can work with the carriers to increase the rate at 
which people----
    Ms. Clarke. Can you provide our committee with those steps? 
That would be helpful.
    Mr. Wagner. Sure. Absolutely.
    Ms. Clarke. Let me ask just quickly because I have run out 
of time.
    Mr. Mina, you spoke in your testimony about impermissible 
bias, and I was just wondering since you used that terminology, 
is there something called permissible bias?
    Mr. Mina. I think if I understand your question correctly, 
the reason why we used that term ``impermissible bias'' is 
because, as Mr. Wagner has talked about and Mr. Romine has 
talked about, there are lots of reasons why there may be 
failure to cause a match, like, you know, for example, 
lighting, environment.
    But our office is really focused on an error that is 
created based on a protected characteristic, like race or sex 
or age. When I make that reference to impermissible bias that 
is what I am referring to.
    Ms. Clarke. So there is no bias that is permissible. In 
other words, if there is a quirk of some sort and you find it 
to be so inconsequential that it becomes part of your standard, 
that becomes permissible bias?
    I am just trying to understand what you mean by 
impermissible bias.
    Mr. Mina. Again, I think what I am focusing on is what is 
actually prohibited by law that our office would look at, which 
is really based on those protected characteristics.
    Now, of course, you know, obviously CBP and folks across 
the Department are trying to eliminate any bias if they find 
any reason. However, in terms of what we do as a policy office, 
we are really focused on the potential for bias in those 
protected areas.
    Ms. Clarke. Very well. I yield back. Thank you, Mr. 
Chairman.
    Chairman Thompson. Thank you.
    The Chair recognizes the gentleman from New York, Mr. 
Katko.
    Mr. Katko. Thank you, Mr. Chairman, and I appreciate you 
having this hearing. This is very important.
    I commend all of my colleagues for their probing questions 
because it is important, but I will make this general 
observation based on my time as a prosecutor.
    When I was first a prosecutor, DNA evidence was this weird 
science thing that no one really knew about, and as we went on 
and as it got refined and as it got better, it became a very 
potent tool not just for law enforcement, but to exonerate 
people who were wrongly accused of crimes.
    I see the biometric technology filling a similar role. It 
is going to help law enforcement. It is also going to do a 
dramatically good thing to prevent misidentification of 
criminal conduct, and I am heartened for that.
    So one of the things I am heartened most about that I heard 
today was from Dr. Romine that the highest-performing 
algorithms have no statistical anomalies, if I understand that 
correctly.
    So that means that at some point those algorithms will get 
to the front lines, and I encourage you to get them to the 
front lines quickly.
    I encourage Mr. Mina never to let your guard down and 
always follow any problems with these systems and make it 
better because in the end, we are all going to benefit.
    I trust my colleagues will ask other probing questions. So 
I have to ask something of Mr. Wagner that occurred yesterday 
that is very important to my constituents, in general, but to 
New York State, in particular.
    A letter was sent February 5, 2020, which was yesterday, to 
New York State saying why Homeland Security can no longer have 
New York driver's licenses as part of the formula for the 
Trusted Traveler Program, and that is because New York State 
under the Green Light Law, which it passed, forbids access by 
CBP and ICE to the New York driver databases.
    So could you briefly summarize for us, and I will ask that 
this letter be incorporated into the record, Mr. Chairman, 
first of all.
    I ask that the letter be incorporated into the record.
    Chairman Thompson. Without objection.
    [The information referred to follows:]
Letter From Chad F. Wolf, Acting Secretary, U.S. Department of Homeland 
                                Security
                                  February 5, 2020.
Mark J.F. Schroeder,
Acting Commissioner, New York State Department of Motor Vehicles, 6 
        Empire State Plaza, Albany, NY 12228, 
        [email protected].
Theresa L. Egan,
Executive Deputy Commissioner, New York State Department of Motor 
        Vehicles, 6 Empire State Plaza, Albany, NY 12228, 
        [email protected].
Via email and U.S. mail

    Dear Mr. Schroeder and Mrs. Egan: On June 17, 2019, the State of 
New York (New York) enacted the Driver's License Access and Privacy Act 
(the Act), effective December 14, 2019.\1\ The Act forbids New York 
Department of Motor Vehicles (DMV) officials from providing, with very 
limited exceptions, pertinent driver's license and vehicle registration 
information to the United States Department of Homeland Security (DHS). 
Specifically, this Act precludes U.S. Customs and Border Protection 
(CBP) and Immigration and Customs Enforcement (ICE) from accessing and 
validating pertinent information contained in New York DMV records that 
is operationally critical in DHS's efforts to keep our Nation secure. 
The Act also threatens to block access to other State law enforcement 
agencies and departments if those agencies or departments provide New 
York OMV records to CBP and ICE.
---------------------------------------------------------------------------
    \1\ N.Y. Veh. & Traf.  201 (2019).
---------------------------------------------------------------------------
    Over the years, CBP has utilized New York DMV records in several 
ways to promote national security and to enforce Federal customs and 
immigration laws. Having access to New York DMV information has enabled 
CBP to validate that an individual applying for Trusted Traveler 
Programs (TTP) membership qualifies for low-risk status or meets other 
program requirements. An individual's criminal history affects their 
eligibility for TTP membership. TTP permits expedited processing into 
the United States from: International destinations (under Global 
Entry); Canada only (under NEXUS); and Canada and Mexico only (under 
SENTRI). TTP also allows quicker processing for commercial truck 
drivers entering or exiting the United States (under FAST). 
Furthermore, CBP has needed New York DMV records to establish ownership 
and thus to determine whether a used vehicle is approved for export.
    The Act prevents DHS from accessing relevant information that only 
New York DMV maintains, including some aspects of an individual's 
criminal history. As such, the Act compromises CBP's ability to confirm 
whether an individual applying for TTP membership meets program 
eligibility requirements. Moreover, the Act delays a used vehicle 
owner's ability to obtain CBP authorization for exporting their 
vehicle.
    Furthermore, on a daily basis, ICE has used New York DMV data in 
its efforts to combat transnational gangs, narcotics smuggling, human 
smuggling and trafficking, trafficking of weapons and other contraband, 
child exploitation, exportation of sensitive technology, fraud, and 
identity theft. In New York alone, last year ICE arrested 149 child 
predators, identified or rescued 105 victims of exploitation and human 
trafficking, arrested 230 gang members, and seized 6,487 pounds of 
illegal narcotics, including fentanyl and opioids.\2\ In the vast 
majority of these cases, ICE relied on New York DMV records to fulfill 
its mission. ICE also needs New York DMV information to safeguard 
Americans' financial and intellectual property rights.
---------------------------------------------------------------------------
    \2\ Nation-wide, last year ICE arrested nearly 4,000 child 
predators, identified or rescued 1,400 victims of exploitation and 
trafficking, arrested 3,800 gang members, and seized 633,000 pounds of 
contraband, including fentanyl and opioids.
---------------------------------------------------------------------------
    New York DMV records have long been used by ICE law enforcement 
personnel to verify or corroborate an investigatory target's Personally 
Identifiable Information (PII), which can include their residential 
address, date of birth, height, weight, eye color, hair color, facial 
photograph, license plate, and vehicle registration information. 
Moreover, ICE's expeditious retrieval of vehicle and driver's license 
and identification information has helped identify targets, witnesses, 
victims, and assets. ICE has used DMV records to obtain search 
warrants, and DMV records are also critical for ICE to identify 
criminal networks, create new leads for investigation, and compile 
photographic line-ups. Additionally, during the execution of search-
and-arrest warrants, ICE officers have used DMV information to identify 
individuals whose criminal history renders them a threat. The Act 
prohibits the sharing of vehicle registration information, including 
the identity of the person to whom the vehicle is registered, with DHS. 
That prohibition prevents ICE from running license plate searches, even 
when ICE is aware that the vehicle's owner has committed a heinous 
crime. In short, this Act will impede ICE's objective of protecting the 
people of New York from menacing threats to national security and 
public safety.
    Although DHS would prefer to continue our long-standing cooperative 
relationship with New York on a variety of these critical homeland 
security initiatives, this Act and the corresponding lack of security 
cooperation from the New York DMV requires DHS to take immediate action 
to ensure DHS's efforts to protect the Homeland are not compromised.
    Due to the Act's negative impact on Department operations, DHS will 
immediately take the following actions:
    (1) Trusted Traveler Programs--Global Entry, NEXUS, SENTRI, and 
        FAST.--Because the Act prevents DHS from accessing New York DMV 
        records in order to determine whether a TTP applicant or re-
        applicant meets program eligibility requirements, New York 
        residents will no longer be eligible to enroll or re-enroll in 
        CBP's Trusted Traveler Programs.
    (2) Vehicle Exports.--Because the Act hinders DHS from validating 
        documents used to establish vehicle ownership, the exporting of 
        used vehicles titled and registered in New York will be 
        significantly delayed and could also be costlier.
    These actions are the result of an initial assessment conducted by 
DHS. We will continue to review Department-wide operations related to 
New York to assess and mitigate the Act's adverse impact on national 
security and law enforcement.
            Sincerely,
                                              Chad F. Wolf,
                                                  Acting Secretary.

    Mr. Katko. Thank you.
    Could you just briefly summarize the contents of this 
letter? Then I have a follow-up question for you.
    Mr. Wagner. So my understanding is New York State because 
of the law that they passed, you know, without consultation 
shut off the access to motor vehicle data, which included 
driver's license information, license plate registration, 
vehicle registration information.
    So in our operations, any of the work that we do where we 
would use that information to help validate an identity, an 
address, and a vehicle, the ownership of a vehicle is impacted 
by not being able to do that directly, and the breadth of our 
mission goes way beyond, I think, what the law says about 
immigration enforcement.
    You are impacting the Customs mission and the National 
security mission, and all the other areas in which we operate.
    Mr. Katko. Is there any other State in the country that is 
having this problem with Customs?
    Mr. Wagner. We have worked some other agreements with other 
States to continue to access the data for, you know, the work 
that we do.
    Mr. Katko. So am I to understand that New York State is the 
only one who forbids Customs and Border Protection as well as 
ICE to have access to their driver databases?
    Mr. Wagner. Yes. It is the only one I am familiar with 
right now.
    Mr. Katko. Even California?
    Mr. Wagner. California, we have a separate agreement with 
where we continue to access their information.
    Mr. Katko. Now, I just want to note further as long as we 
have a couple of minutes here some of the things that are in 
the letter. Tell me if this is correct.
    On a daily basis, ICE uses New York DMV data in an effort 
to combat transnational gangs, narcotics smuggling, human 
smuggling and trafficking, trafficking of weapons and other 
contraband, child exploitation--child exploitation?--
exploitation of sensitive technology, fraud, and identity 
theft.
    Is it fair to say that by not having access to the 
database, it hampers those investigations at times?
    Mr. Wagner. Sure. Any law enforcement practice where you 
would normally use that information, yes, would be impacted.
    Mr. Katko. I yield back the balance of my time.
    Chairman Thompson. Thank you.
    The Chair recognizes the gentlelady from New York, Miss 
Rice, for 5 minutes.
    Miss Rice. Thank you, Mr. Chairman.
    Let's continue, Mr. Wagner, if we can, talking about what 
happened with New York.
    Was CBP made aware of the policy before the Acting 
Secretary's announcement on Fox News?
    Mr. Wagner. Yes.
    Miss Rice. So you were aware of it. No notification was 
made to Congress about blocking access to these Federal 
programs for New Yorkers?
    Mr. Wagner. I do not know.
    Miss Rice. Well, there was none.
    So personally, we, my office has already received an influx 
of new questions about this policy literally overnight. Fifty 
to 80,000 New York State residents are affected who have 
pending global entry enrollment applications or renewals.
    This is going to have an enormous impact on people, many of 
whom entered into this program because their jobs require them 
to travel internationally.
    So what do you plan to do about all those people who are 
going to be impacted?
    Mr. Wagner. Well, without the ability to help validate 
their identity through the----
    Miss Rice. You have their fingerprints.
    Mr. Wagner. Yes, but if they have not been arrested, the 
fingerprints do not tell us anything. What would the 
fingerprints tell you if you have not been arrested?
    Miss Rice. So what are you trying to find out is my point.
    Mr. Wagner. Trying to validate their address where they 
live, their residency. These are things important to us as we 
establish that low-risk Trusted Traveler status that we afford 
people in that program. Without the ability to do that, how 
would we do that?
    So New York State shut off without consultation our access 
to that information in December. How would we continue to 
operate and validate who people are?
    Miss Rice. Well, going forward, what about the people who 
already have it?
    I have global entry. So when I go to renew it, I am not 
going to be able to do that.
    Mr. Wagner. Correct.
    Miss Rice. Yet here I am, a sitting Congresswoman with 
global entry. So to me, to me, to me, I understand the 
distinction that you are making. There are at least 15 other 
States you are saying that you have individual agreements with 
all of them where they do not block access to this database? 
Fifteen other States who have a global----
    Mr. Wagner. I am not aware of any other State blocking our 
access to that information.
    Miss Rice. OK. So I would like you--we are going to follow 
up. I am going to follow up directly with you because there are 
at least 15 other States that allow undocumented people to get 
driver's licenses.
    Mr. Wagner. Yes.
    Miss Rice. I would----
    Mr. Wagner. I am not aware of them blocking our 
information.
    Miss Rice. OK. So you not being aware is not a sufficient 
answer because there could be other States that do, and it 
seems to me that this is, once again, an attempt by this 
administration, specifically Donald Trump, who formerly was a 
New Yorker, to punish New York.
    So you and I are going to follow up on this, and I 
appreciate you trying to answer these questions, but we need 
more information, and I appreciate your attempt to answer these 
questions.
    I yield back. Thank you, Mr. Chairman.
    Chairman Thompson. Thank you very much.
    Mr. Wagner, just for the record, can a person have global 
entry without a driver's license?
    Mr. Wagner. Yes, I believe so.
    Chairman Thompson. So I am trying to figure out how you are 
going to cancel all of these people and some of them do not 
even drive and deny them.
    Mr. Wagner. Well, it is a New York State identification.
    Chairman Thompson. But they have passports.
    Mr. Wagner. Validation of that information.
    Chairman Thompson. But they have a passport. They have a 
passport.
    Mr. Wagner. How do we validate the address of where they 
live?
    Chairman Thompson. My driver's license has a post office 
box. So, I mean, I am just trying to figure out are you being--
--
    Mr. Wagner. Why is the information blocked for this purpose 
then?
    Chairman Thompson. Well, I do not know. I am saying why 
would you cancel it if it----
    Mr. Wagner. Well, why would New York State block the 
information for this purpose?
    Chairman Thompson. Is it for identification or security?
    Mr. Wagner. Both.
    Chairman Thompson. But you can prove it with other 
documents. I mean, that is what I am trying to figure.
    Well, the Chair recognizes the gentleman from Louisiana, 
Mr. Higgins.
    Mr. Higgins. Thank you, Mr. Chairman.
    I yield 1 minute to my colleague, Mr. Katko.
    Mr. Katko. Thank you, Mr. Higgins.
    Just have a quick follow-up question with my colleague, and 
it is a quite simple one really.
    First of all, it is clear that it hampers investigations 
with ICE. It is clear that it hampers the ability to get 
certain identification that is available in driver's DMV 
database in New York State.
    I just want to make sure that it is clear. My colleague 
from New York, Miss Rice, mentioned that there are many other 
States that have possible--like allowing illegal aliens to get 
driver's licenses.
    That is not the issue. The issue is, is there any other 
State in the United States of America that completely blocks 
Customs and Border Protection and ICE's access to DMV records.
    Mr. Wagner. I do not believe so.
    Mr. Katko. OK. So in my opinion, and I have an immense 
amount of respect for my colleague from New York, I do not 
believe this is a political exercise. All New York would have 
to do is enter into a similar agreement that those other 15 
States have entered into with Customs and Border Protection and 
ICE where they simply verify that they will not use it for 
immigration enforcement purposes, but use it for law 
enforcement purposes and for global entry and those types of 
things.
    Is that correct? You can do that?
    Mr. Wagner. I think that is a discussion we would have with 
the State.
    Mr. Katko. OK. You have done it with other States?
    Mr. Wagner. Yes.
    Mr. Katko. OK. Thank you.
    I yield back.
    Mr. Higgins. I thank my colleague.
    Just to follow up on the New York question because it is 
just a fascinating topic, are you aware of negotiations or 
communications prior to the New York legislative body passing 
this law with Customs and Border Protection?
    Were we out front with this communication at all?
    Mr. Wagner. Our access----
    Mr. Higgins. It seems to me like they should have known 
before they passed the law this was going to happen.
    Mr. Wagner. Right. Our access was just turned off one day 
in December, and our officers and agents in the field called in 
and said, you know, ``What happened to our access?''
    Mr. Higgins. So you are saying that as far as you know, and 
you can certainly advise if you do not know or have no way of 
knowing, but as far as you know, sir, was there an on-going 
communications during the course of the development of this 
legislation in the State of New York with the law enforcement 
agencies like Customs and Border Protection and ICE?
    Mr. Wagner. I do not know. I am not aware of any.
    Mr. Higgins. Well, one would hope that there was.
    Dr. Romine, you mentioned black box texting. Would you 
clarify that that means that as your products are tested 
through NIST, your facial recognition products provided by 
vendors, that they are tested without your knowledge of who the 
vendor is? You are strictly looking at the results of the 
algorithms themselves?
    Mr. Romine. So when I use the phrase ``black box testing,'' 
what I mean is that we do not have any insight into the 
characteristics of the algorithm itself. We publish an API, an 
application----
    Mr. Higgins. Do you know the identity of the vendor?
    Mr. Romine. It is self-identified.
    Mr. Higgins. It is self-identified as you are studying the 
product itself.
    Mr. Romine. That is correct. We do that.
    Mr. Higgins. OK. Just to clarify that.
    Now, can any vendor submit an algorithm to NIST for 
testing?
    Mr. Romine. Yes, sir.
    Mr. Higgins. The process by submitting that product is 
standardized?
    Mr. Romine. It is, sir.
    Mr. Higgins. All right. With the top-performing algorithms 
like Customs and Border Protection uses, is there a wide 
variance between what you are referring to as the top-
performing algorithms and, say, academic projects perhaps 
submitted for testing?
    Mr. Romine. Yes, sir. There is a wide variance in the 
performance of algorithms at the top.
    Mr. Higgins. So in your scientific assessment of NIST 
testing and evaluation of facial recognition technologies, 
would you say that what we are referring to as the top-
performing algorithms that are being used by Customs and Border 
Protection are far and beyond some of the common products that 
are presented to you?
    Mr. Romine. The top-performing algorithms are significantly 
better in their error rate.
    Mr. Higgins. Can you confirm for this committee, sir, that 
it is, indeed, the top-performing algorithms at this point that 
are being used by Federal law enforcement agencies?
    Mr. Romine. Sir, I have no way to independently verify 
that.
    Mr. Higgins. But would you say that Customs and Border 
Protection is using the top. I want to confirm.
    Mr. Romine. I did not say that.
    Mr. Higgins. Can you confirm that, good sir?
    Mr. Romine. We are using not the algorithm they tested, but 
we are using the previous version of it, and we are switching 
to, we are upgrading to the version that they tested next 
month.
    So, yes, we are using a high-performing vendor.
    Mr. Higgins. Going to the next iPhone? All right. I think 
that vaguely answers my question, and my time has expired.
    Mr. Chairman, thank you.
    Chairman Thompson. Thank you very much.
    The Chair recognizes the gentleman from New Jersey, Mr. 
Payne.
    Mr. Payne. Thank you, Mr. Chairman.
    Let me ask. Who and where is all of this facial recognition 
data stored?
    Please describe under what specific circumstances this data 
is allowed to be shared or used or transferred, if that is the 
case?
    Mr. Wagner. We are using as a database travel document 
databases. So these are photographs collected by the U.S. 
Government for the purposes of putting on a travel document, 
like a U.S. passport or a U.S. visa that is issued to a foreign 
national or a photograph of a foreign national when they arrive 
in the United States, like under the Visa Waiver program.
    We would take their photograph or read the photograph from 
the chip in their passport and store that. That is what forms 
the baseline gallery that we match against.
    Now, new photographs we take of a person, U.S. citizen, if 
we match it to a U.S. passport or a U.S. identity, those photos 
are discarded, OK, after 12 hours just for some system work.
    If you are a foreign national, that goes over to a system 
called IDENT that DHS runs where they are stored under the 
protocols of the Systems of Record notice of the data retention 
period of that, which I believe is 75 years.
    Mr. Payne. OK. All right. To follow up with that, you know, 
we are living in an age where everything is being hacked. What 
type of security measures or protections have been put in place 
regarding the security of this data?
    Mr. Wagner. So the databases are housed within the U.S. 
Government. CBP does not necessarily keep or own any of those 
permanent databases. You know, they are owned by Department of 
State. They are owned by other branches of DHS.
    We access a lot of that information. We use it. We match 
against it, and then we put information back into them.
    Mr. Payne. OK. You know, I continue to have hits come 
across my desk about the mishaps and disadvantages of facial 
recognition technology and the racial bias. It is my 
understanding that the technology continues to misrepresent and 
irregularly identify people of color and women.
    So am I hearing from the majority of the panel that that is 
not the case? Because it keeps coming to us. So there has to be 
some validity.
    Mr. Romine. Sir, in our testing for the one-to-one 
identification algorithms, we do see evidence of demographic 
effects, differences with regard to race and sex and age.
    Mr. Payne. OK.
    Mr. Romine. In the one-to-many identification testing that 
we did for the algorithms that we tested, there was a small set 
of high-performing algorithms that had undetectable 
differentials.
    Mr. Payne. OK.
    Mr. Romine. But the majority of the algorithms still 
exhibit those characteristics.
    Mr. Payne. Can you give a description of the difference 
between the two sets?
    Mr. Romine. Yes, sir. In the case of verification, 
verifying an identity, a biometric is matched solely or in the 
case of face recognition a picture is matched against----
    Mr. Payne. Is that the one-to-one?
    Mr. Romine. That is the one-to-one.
    Mr. Payne. All right.
    Mr. Romine. The identification or the verification is to 
try to determine if you are who you say you are.
    Mr. Payne. All right.
    Mr. Romine. It is matched against a gallery of one, in 
essence.
    Mr. Payne. What is the one-to-many?
    Mr. Romine. The one-to-many is matched, in the case of 
CBP's application, one to perhaps thousands for the airline 
public, the traveling public, or one to millions in the case of 
law enforcement, such as FBI, to try to identify a suspect.
    Mr. Payne. So you are saying that the percentage of 
identifications in the one-to-one, you have more incidents of 
this bias that we see?
    Mr. Romine. I should clarify. In the algorithms that we 
tested, that is correct. However, many of the vendors who chose 
to participate in the one-to-many testing did not choose to 
participate in the one-to-one, and those are some of the 
highest-performing in the one-to-many.
    Mr. Payne. OK. Thank you.
    I yield back, Chairman.
    Chairman Thompson. Thank you.
    The Chair recognizes the gentleman from North Carolina, Mr. 
Walker.
    Mr. Walker. Thank you, Mr. Chairman.
    I would like to yield 1 minute to the gentleman from 
Louisiana.
    Mr. Higgins. Thank you, my colleague.
    Dr. Romine, I have a question regarding the effectiveness 
of the technology that you have tested regarding children.
    Is it a potential if we assembled a gallery of photographs 
of children crossing the border, some of whom are being 
exploited and false identifications presented; how does the 
technology work with children compared to mistake and errors in 
other demographics?
    Can this technology be used to protect children that are 
perhaps being exploited crossing our borders, coming into our 
country?
    If so, what can we do to protect the privacy of those 
children, given the fact that they are minors?
    I will leave you my remaining 30 seconds here.
    Mr. Romine. Thank you, sir.
    The application specifically is something that we do not 
test. What we have tested is the effectiveness of the 
algorithms in terms of error rates.
    We do find that for children in the one-to-one setting, the 
one that you just described, there are demographic effects 
there. There are differentials. The error rates are higher in 
the one-to-one case with respect to age.
    So it is more difficult. Based on our testing, it appears 
more difficult to match.
    Mr. Higgins. But there is no gallery. There is no one-to-
many. There is no gallery of the photographs that you have.
    Mr. Romine. We have no such gallery.
    Mr. Higgins. If we did develop that, then NIST could test 
the effectiveness and perhaps this could be a tool to protect 
children?
    Mr. Romine. We could. We could undertake many different 
kinds of testing to determine the effectiveness of those.
    Mr. Higgins. Thank you, sir.
    I thank my colleague for yielding.
    Mr. Walker. Absolutely. Thank you, Representative Higgins.
    Mr. Wagner, is it true that a biometry entry/exit system 
uses less personally identifiable information than the current 
system that we have in place?
    Mr. Wagner. Yes, because currently you open your passport 
booklet and show it to an individual to either, say, check your 
bags, go through TSA screening, board the plane, a CBP officer. 
You are exposing your name, your date of birth, your passport 
number, your place of birth, all the information on your 
passport page.
    Somebody could be looking over your shoulder. Somebody 
could take a picture over your shoulder looking at that. You 
are disclosing it to a person who does not actually need to 
know all of that additional information versus standing in 
front of a camera with no identifiable information other than 
your face, which they can already see, and your picture is 
taken and on the screen comes a green checkmark, and that 
person now knows you have been validated by the Government 
record to proceed.
    So you are sharing actually less information in this 
instance.
    Mr. Walker. But not only sharing less information, but on a 
scale of 1 to 10, 10 being the highest, how would you rate this 
progress as, in your own words, continuing to develop and 
rightfully so, would be the highest security possible for 
travelers compared to anything else that we are doing now?
    Mr. Wagner. Right now I think on top of everything else we 
are doing, it brings us closest to 10, which is where we want 
to be.
    Mr. Walker. When Representative Higgins talked about some 
of the children involved, are there any numbers or statistics 
based on people that you have caught either involved in human 
trafficking or some other nefarious activity because, strictly 
because of the facial recognition?
    Mr. Wagner. Yes. On the land border, we have got 247 
imposters so far, meaning they had a legitimate document that 
belonged to somebody else. Eighteen of those, so 7 percent, 
were under the age of 18. So they would be considered children.
    Seventy-three of those at the land border had U.S. 
passports or U.S. passport cards, and 46 of them, or almost 20 
percent, had criminal records that they were trying to hide.
    Mr. Walker. Do you believe these were identified strictly 
because of the use of facial recognition or was there any other 
aspect or involvement?
    Mr. Wagner. Our officers are also very good at identifying 
the behaviors in the person when they present the travel 
document. A lot of times that can also be a cue that the 
person's hiding something.
    But the technology on top of officer's skills and abilities 
should bring us to that security posture that will bring us to 
near perfect.
    Mr. Walker. Are there any policy's difference between a 
U.S. citizen versus non-citizen?
    Mr. Wagner. Well, everyone has to establish their identity 
by law. Everyone has to produce some type of identification. 
The law requires a U.S. citizen to travel on a passport.
    Mr. Walker. In the process, scrubbing this 12 hours after 
is what?
    Mr. Wagner. That is our internal policy. We take a new 
picture. We discard it after 12 hours. We are looking at 
actually shrinking that to a less time. We only keep it there 
in case the system crashes and we have got to restore 
everything.
    Mr. Walker. Thank you, Mr. Wagner.
    I yield back, Mr. Chairman.
    Chairman Thompson. Thank you.
    The Chair recognizes the gentlelady from Las Vegas, Ms. 
Titus.
    Ms. Titus. Thank you, Mr. Chairman.
    I find this interesting. The more you talk the less I know, 
it turns out, unfortunately.
    McCarran Airport is in my district. It is a very busy 
airport, one of the busiest in the country. A lot of 
international tourists come through there.
    So I know we have talked a lot about the use of this facial 
recognition for security reasons. I would like to talk about it 
in terms of how it affects the passengers' experience. We want 
people in Las Vegas to have a good experience from the time 
they land until the time they leave.
    So how do you work to coordinate using this for security 
and also reducing wait times or serving the passenger as 
opposed to making it more difficult for the passengers?
    Mr. Wagner.
    Mr. Wagner. So it absolutely supports our travel and 
tourism goals as well. It makes a much better passenger 
experience, a more convenient passenger experience, a more 
consistent passenger experience.
    You think as you go through the airport the number of stops 
you have to make to produce a piece of paper or open your 
passport again or provide some other form of validation to go 
forward.
    You can use the facial recognition and the camera to have 
that same process. It is quick enough that you walk up and your 
picture is taken and 2 to 3 seconds you are moving forward.
    So what we are seeing is reduced wait times. The airlines 
as they incorporate it into the boarding process are reducing 
their boarding times over the aircraft sometimes as much as, 
say, 40, 45 percent.
    It is a different atmosphere for the travel because you are 
not fumbling for your documents or forgetting where you put 
your boarding pass or getting stuck in line behind the person 
whose phone went dead when they went through to read their 
boarding pass or forgot where they put their passport.
    So it is creating a better atmosphere for the traveler. It 
is moving the lines quicker because you cannot leave your face 
on the plane. You cannot, you know, leave your face in the 
bathroom. You cannot forget that like people do with their 
travel documents.
    So it is making an easier process because everybody knows 
how to take a picture, and what we see is people are enjoying 
this process a lot better for them, and what we are seeing is 
the lines reduced.
    Ms. Titus. Are you working with TSA or local law 
enforcement to make this all run smoothly or is that not 
necessary?
    Mr. Wagner. So we are working very closely with TSA. We 
have run a few pilots with them. We have an on-going pilot in 
Atlanta because we build the gallery as the person prints out 
their boarding pass. So anyplace now where they have to show 
their passport at the airport, say, when they are departing the 
United States, you could take a picture and validate it against 
our gallery.
    So you are outside of the airport or you just walked into 
the airport. You got your boarding pass. Your picture goes into 
that gallery.
    So steps like checking your bags where you have to show 
your ID to the airline person, you can have a camera there that 
does that.
    You go up to the TSA checkpoint. TSA can take a photograph. 
It transmits to our gallery, again, because we built it for the 
biometric exit requirement, but we want to make that 
environment available to all the other places in the airport 
where you would show your passport to do that.
    So, yes, so for TSA you could take a picture. Then you go 
through screening. You go to board the plane. The airline takes 
your picture. It comes back to our gallery. We confirm it. You 
can board the plane without even showing your passport to the 
airline or showing your boarding pass to the airline.
    Ms. Titus. Well, suppose you find somebody does not match. 
My understanding is this goes through an app, and law 
enforcement, if they are busy or if the person responsible for 
checking out the non-match is doing something else, do you have 
some kind of staffing model for who is responsible for that?
    Because I had heard it comes through an app, and since 
there is no action that you are supposed to take that is very 
clear, sometimes they just ignore it.
    Mr. Wagner. Depending on where you are in the airport, 
generally it would be the airline or CBP, we would just look at 
the physical passport, which is what you are presenting now, 
and we would make a determination.
    Now, if we have doubts about do you match the picture on 
your passport, right, which happens, or if the airline has 
doubts you match the picture on your passport, they may call us 
over. We may ask the person for another form of ID. We may ask 
them additional questions. We may do a further inspection on 
them.
    So if you do not look like your passport photo, you know, 
from a visual review, these are the same kind of things that 
would occur.
    Ms. Titus. We have been having a lot of confusion about 
going to the Real ID from just regular driver's licenses. 
People do not know they have to do that. We are trying to get 
the word out.
    Some States did not provide the funding to go to Real ID. 
Is that transition part of your consideration as you develop 
this new system or is it not connected?
    Mr. Wagner. It is separate than this.
    Ms. Titus. So that it is not going to make any difference?
    Mr. Wagner. Not really.
    Ms. Titus. All right. So people who will use their 
passports instead of Real ID, that will not matter?
    Mr. Wagner. Right, because these are international 
travelers we are talking about today. So they would generally 
have a passport.
    Ms. Titus. You do not see this moving to national as well 
as international once it is up and running?
    Mr. Wagner. I would defer to TSA on that for their 
requirements on how this might apply to a domestic flight.
    Ms. Titus. OK.
    Mr. Wagner. I think there is some good discussion to have 
there, that if people have passports and you could 
electronically confirm them, even on a domestic flight, should 
the traveler opt into this, I think it would be good government 
to build a system like this if that is what people would want.
    Ms. Titus. Well, thank you.
    Thank you, Mr. Chairman.
    Chairman Thompson. Thank you.
    The Chair recognizes the gentlelady from Illinois, or I am 
sorry. The gentleman from Texas, Mr. Crenshaw.
    Mr. Crenshaw. Thank you, Mr. Chairman.
    Thank you all for being here. It has been an interesting 
hearing to watch.
    I just want to dispel any misinformation on the facial 
recognition technology that we are discussing here today. It 
seems to be abnormally controversial.
    We are not talking about 1984-style Government 
surveillance, not like China has. We are not talking about 
facial recognition at the National Mall or Times Square or 
downtown Houston.
    We are talking about facial recognition at air, land, and 
sea ports of entry, where the Government has not just the 
authority but the duty, the responsibility to know who enters 
our country and where they are already checking for 
identification, of course.
    It seems from the answers we have gotten that CBP is using 
the best algorithms with almost no bias whatsoever in them. 
That is what we have established today as far as I understand.
    Locations where facial recognition technology is employed, 
those locations are marked, correct?
    Mr. Wagner. Yes. It is where you would normally present 
your passport.
    Mr. Crenshaw. OK. Locations where facial recognition 
technology is employed where entrants are required to present, 
and you already answered that one, present a form of 
photographic identification already.
    Entrants are allowed to opt out of facial recognition 
technology and present photographic identification to a CBP 
officer who will then compare the physical appearance of the 
entrant and the photographic identification presented, correct?
    Mr. Wagner. Correct.
    Mr. Crenshaw. Biometric data for U.S. persons is stored for 
no more than 12 hours in an encrypted virtual private cloud, 
correct?
    Mr. Wagner. Correct.
    Mr. Crenshaw. Biometric data for entrants who are not U.S. 
persons are stored in IDENT, correct?
    Mr. Wagner. Correct.
    Mr. Crenshaw. Giving the above and knowing where facial 
recognition technology is used, requirement to present photo 
ID, the ability to opt out, and a secured storage, what are the 
major privacy concerns I might be missing?
    How can we improve this?
    Mr. Wagner. I think what we have heard from the privacy 
community is people get used to the convenience of this 
technology and that bleeds over into the commercial world or 
their private sense, and they may be more likely to allow that 
to happen outside of the Government requirements.
    You know, in my discussions with them, I said, ``Yes, but 
there is also an expectation by the public that they have this 
convenience in their private life and why should their 
interactions with their Government be so antiquated?''
    Mr. Crenshaw. Yes.
    Mr. Wagner. Why should their travel through the airport be 
so antiquated and manual and frustrating?
    You know, do they not expect that that same convenience 
should apply when they are traveling internationally?
    Mr. Crenshaw. One way this could be viewed in a very 
positive sense is to combat human trafficking. Is there a way 
that tools like this can be integrated with other tools like 
Spotlight and SAFER to battle child sex trafficking, human 
trafficking?
    Mr. Wagner. Sure. Because what this helps us do is our core 
vetting processes are biographically-based, right? A name and 
date of birth is submitted, say, to a watch list, you know, 
through an airline application, through TSA, and we vet and do 
those background checks on the basis of, say, who the airlines 
tell us who is flying, so who checked in, who purchased a 
ticket.
    But when you can then use a biometric to validate that you 
vetted the right person, you have the assurances that that is 
the person who is actually traveling and not just their 
passport is traveling under a different person that is being 
trafficked.
    So it helps us close those vulnerabilities of imposters for 
nefarious or being trafficked or being victimized to be able to 
do that using imposter documents.
    Mr. Crenshaw. In my limited time left, can this be used to 
combat visa overstays as well?
    Mr. Wagner. Yes. Now, we track visa overstays primarily 
through the biographic information the airline provides, but by 
implementing this system, we have actually biometrically 
confirmed almost 44,000 by overstays. With the biometric 
validation that these people overstayed, they end up leaving 
the United States, albeit late, later than they were authorized 
to do so. So, you know, just about 44,000.
    Mr. Crenshaw. Thank you. I yield back.
    Chairman Thompson. Thank you.
    The Chair recognizes the gentleman from New York, Mr. Rose.
    Mr. Rose. Thank you, Mr. Chairman.
    Mr. Mina, the NYPD has in the past used facial recognition 
to compare photos from crime scenes against its own internal 
arrest databases. Some State lawmakers want to take that 
ability away from the NYPD and other New York State law 
enforcement agencies.
    Do you support police agencies using facial recognition in 
the course of their criminal investigations?
    Mr. Mina. Congressman, that is not necessarily an issue 
that we have looked at at CRCL. We are primarily looking at the 
DHS uses of facial recognition technology and, in particular, 
we have focused primarily, as Dr. Romine mentioned, less so on 
the identification piece where you have sort-of you are trying 
to match a photo to a gallery of, you know, tons, and we are 
looking at a much narrower. We are looking at, I think, more of 
the verification, if I understand the technology correctly.
    Again, our role there is really to make sure that we are 
addressing these concerns regarding impermissible bias, whether 
that is, again, based on race, national origin, age, gender, as 
we have talked about.
    Mr. Rose. So one thing that I think has been absent in this 
conversation is the ways in which civil liberties can 
potentially be infringed upon in the absence of the use of 
technology. Can you speak to this for a minute or two?
    I am thinking of false positives. I am thinking of people 
who are being arrested or at least questioned further based off 
of just a verbal description.
    Mr. Mina. Absolutely, Congressman. So I think that it is 
obviously critically important to blend both the use of 
technology as well as the end-user in this process.
    I do not think it is an either/or proposition, and as we 
have advised CBP and other DHS components, that is, from a 
policymaking perspective, that is really where we see the 
greatest benefit, is really that interaction between the 
technology and the user.
    Because, as Mr. Wagner talked about earlier, for example, 
if there was a false negative, for example, then you would have 
the line officer looking or agent looking at their actual, you 
know, passport or other travel documentation and making that 
independent verification.
    Mr. Rose. Sure.
    Mr. Mina. Then if it matches, the person goes along and 
they board the flight.
    Mr. Rose. Right. I think that it is important to know then 
so that we are all on the same page that the use of technology 
has consistently been implemented to preserve our public 
safety, but also to further protect civil liberties.
    This is being lost in this conversation as yet again I 
think we are unnecessarily politicizing an effort to keep us 
safe.
    It is not perfect, and you all have some work to do to make 
it even better, and I am encouraged to hear that you are making 
it better.
    Mr. Wagner, you are going to have to hear from another New 
Yorker. So look. I am not a supporter of this New York 
legislation that was passed. I think it is unfortunate and 
wrong that you all were not notified, but two wrongs do not 
make a right.
    So I am going to ask some very simple questions. If you all 
were setting out to be the professional force that you are and 
do this professionally, do you think that in advance of 
announcing this you should have told Congress what was wrong 
and what would happen if it was not fixed or addressed?
    Mr. Wagner. I would have to defer to DHS on that.
    Mr. Rose. No. Come on, man. This is ridiculous. It is a 
simple question. That is a simple question.
    We heard about this from Fox News. This is politics at its 
worst. We are talking about acting like professionals right 
now.
    If there is a problem that needs to be addressed and you 
all are doing this, do you think it was appropriate that we 
were not told well in advance so we can try to arrive at some 
solution?
    Do you think that is OK? Is that the way you would want to 
carry this out?
    Mr. Wagner. I am not going to comment on that. I mean, that 
is your----
    Mr. Rose. You are not going to comment. So by the fact that 
you have given very clear and declarative answers previously, I 
think that we can all assume what you are thinking and 
unwilling to say right now.
    So let's commit to actually trying to solve problems here. 
You have got Members of Congress that will not be able to renew 
something. You have got more important than Members of 
Congress. Who cares about Members of Congress? Millions of 
other people that are now held in the balance, people on my 
staff, people, colleagues, all types of people, all types of 
people.
    This is politics. If you really were making an effort to 
address a problem, to address a problem, there would have been 
a system, a proposal, a negotiation, a conversation, letters 
written. That is the way business is conducted.
    So let's put that aside. Would you now commit, now that we 
have all engaged in our politics, to actually having sensible 
meetings and conversations about a way forward to solve this 
issue?
    Mr. Wagner. Sure, I think that is a good point.
    Mr. Rose. You would commit to that. OK. Thank you.
    Chairman Thompson. The gentleman's time has expired.
    The Chair recognizes the gentlelady from Illinois, Ms. 
Underwood.
    Ms. Underwood. Thank you, Mr. Chairman.
    Many of my constituents in Northern Illinois have to drive 
over an hour to get to a major airport in Chicago and, 
therefore, we are always interested in learning more about 
technologies that can improve airport security wait times, but 
biometric data is ripe for potential abuse and misuse, which is 
why it is so important to ensure that DHS uses facial 
recognition and other technologies in a fair and reliable and 
effective way.
    Mr. Wagner, although children under the age of 14 are not 
required to be screened, many do go through screening that 
collects their biometric information.
    How does CBP store and secure this information? I am 
talking about under 14.
    Mr. Wagner. I think if you are outside the scope of the 
biometric tracking requirement, which is 14 to 79, I believe we 
discard all of that information. Let me verify that.
    Ms. Underwood. Yes. Would you be willing to provide the 
committee with that information in writing?
    Mr. Wagner. Yes.
    Ms. Underwood. Both the procedure and the policy in order 
to do so?
    Mr. Wagner. Yes.
    Ms. Underwood. OK. Are there any differences in how CBP 
collects, uses, or secures children's biometric information in 
comparison to adults?
    So if a child presents, does it take it and immediately 
release, right, or is it going to be going through some kind of 
filtering later on?
    We want that level of information.
    Mr. Wagner. OK. You have got it.
    Ms. Underwood. OK. The December 2019 NIST report found that 
children are more likely to be misidentified during biometric 
screening.
    Of course, we know that other groups, like we have 
discussed today, people of color, seniors, are also 
misidentified.
    Mr. Wagner, what actions is CBP taking to correct the 
patterns of errors identified in the NIST report?
    Mr. Wagner. Well, again, we are using a high-performing 
algorithm that we are not seeing those demographic-based error 
rates.
    Now, if someone does not match to either the gallery or to 
the document they are presenting, we will physically examine 
the document.
    Ms. Underwood. Right.
    Mr. Wagner. Manually look at the picture, and if we have 
the confidence it is the person, we can do that through 
questioning. We could do that through additional forms of 
identification. We can do that through an inspection of the 
person.
    Sometimes it is just looking at the passport and going, 
``OK. That is you. Go ahead.''
    It all depends on how discrepant you look from your travel 
document photograph.
    Ms. Underwood. Some passengers report being unaware or 
confused about how to opt out of their biometric screening. As 
CBP expands the biometric screening program, does it intend to 
reevaluate the best method of communicating the important opt 
out information to passengers?
    Mr. Wagner. Yes. So right now we have got signage at the 
airports, but you know, a lot of people do not read signs at 
the airport.
    We have got gate announcements that the airlines try to 
make before boarding, but again, there is always competing 
announcements going on, and sometimes it is tough to understand 
what is being said.
    So we are actually looking with the airlines as could we 
print things on the boarding pass.
    Could we give notifications when they are, say, booking 
their ticket or when they are getting their check-in 
information for boarding?
    Are there electronic messages we could provide?
    Ms. Underwood. Right.
    Mr. Wagner. So we are looking at additional ways to do 
that.
    We also started taking out some privacy advertisements 
advising people of the requirements and what their options are 
as well, too.
    Ms. Underwood. OK. Well, it is certainly my interest in 
making sure that every passenger understands that, No. 1, this 
is happening and, No. 2, that they have a choice to opt out, 
and I would certainly urge the CBP to strongly consider and 
issue this committee a time line for perhaps outlining how we 
can improve that communication to all passengers.
    Does CBP capture and report the rate of false positives or 
mistaken identifications among different demographics at each 
port of entry where biometric technology is used?
    Mr. Wagner. What we track are the people that we take a 
photograph of or receive a photograph of.
    Ms. Underwood. Right.
    Mr. Wagner. And we are not able to match it to their travel 
document that is in our gallery.
    Ms. Underwood. Right.
    Mr. Wagner. Again, that is that 2 to 3 percent.
    Our review of that information does not show noticeable 
discrepancies on any types of----
    Ms. Underwood. That was not my question. My question is 
capturing and reporting by port of entry. So we want to know 
the false positives. Are we seeing more at certain places along 
the border?
    Are we seeing more false positives at certain airports?
    Mr. Wagner. We are not seeing false positives that is 
matching you to a different identity. We are not seeing that 
with this technology.
    Ms. Underwood. Or mistaken identities?
    Mr. Wagner. We are not seeing that. We are more likely you 
do not match against anything. So we get a no information 
return.
    Ms. Underwood. OK. Dr. Romine, can you elaborate on what 
NIST recommends to algorithm developers to improve accuracy 
across demographics?
    Mr. Romine. The report, the testing that we do does not 
result in recommendations specifically to the vendors other 
than to take the data that we provide, the evaluation results, 
and strive to use those results to improve their methods. But--
--
    Ms. Underwood. So you are saying that you do not have a lot 
of interaction with the developers?
    Mr. Romine. We have informal interaction with them in the 
sense that the scientists who do this biometric testing are 
part of a larger biometrics community. We see the vendor 
representatives, the scientists at meetings, and so on.
    But with regard to the FRVT itself, the testing, the 
feedback that we provide to the vendors is the test result.
    Ms. Underwood. OK. So you all are not doing like convenings 
with industry and helping them improve the quality of their 
product?
    Mr. Romine. We do host events, but more as a convener to 
get the community together to discuss different techniques. But 
we do not provide, other than sort-of in the general scientific 
community sense, we do not provide specific recommendations for 
their improvement.
    Ms. Underwood. OK. I recognize my time has expired. We 
would just like to get more information about that in writing.
    Mr. Romine. Happy to do that.
    Ms. Underwood. Thank you, sir.
    Chairman Thompson. Thank you very much.
    The Chair recognizes the gentlelady from New Jersey, Mrs. 
Watson Coleman.
    Mrs. Watson Coleman. Thank you, Mr. Chairman.
    Thank you for your testimony.
    A couple of questions. I think I want to just talk more 
about the role of the CRCL and NIST. It seems to me that there 
has not been much coordination across the DHS spectrum of 
directions from DHS to each component regarding their 
deployment of biometric technologies. You can correct me if I 
am wrong.
    Is there any sort of Department-wide strategy in place for 
the use of biometric technologies or are components like yours 
given wide latitude to stand up biometric programs as you 
please?
    Mr. Wagner. I am sorry?
    Mrs. Watson Coleman. Are you a Lone Ranger?
    Mr. Wagner. Are we what? I am sorry. I did not hear that.
    Mrs. Watson Coleman. OK. It does not seem like there is 
coordination. It does not seem like there is this sort-of 
Department-wide oversight.
    I want to know whether or not you are getting directions 
from others because then I am going to ask Mr. Mina what is 
your role and to what degree have you been involved in the 
oversight and in signing off on how these things are being 
done.
    Mr. Wagner. Yes. So as we build out new programs, we are 
bound by certain statutes that require us to publish, say, your 
Systems of Record notice, your privacy impact assessment, 
where, you know, things are reviewed by, you know, our internal 
counsel or our Privacy Officer, and to make sure we make and 
meet all of the requirements of the statutes.
    Do you have the authority to collect what you are doing?
    You know, is your time line for storing it and sharing it, 
is that all permissible in law?
    Is it consistent with your mission? Are you authorized to 
do those things?
    Mrs. Watson Coleman. So are you operating within your sort-
of silo?
    This is what the law says with regard to what you can do. 
Is this how you execute based upon what your interpretation is 
of that or is there a DHS component that plays into this as 
well and says, ``OK. But this is how we want to see this''?
    Mr. Wagner. Well, depending on like the acquisition 
process, there is a multitude of people at DHS that look at the 
acquisition, the resources spent.
    There is a whole process to go through for approval before 
various boards that authorize the expenditures and the 
investment in that. There is the DHS privacy officer. There is 
DHS counsel. So there is a lot of oversight by DHS already in 
this process.
    Certainly the rulemakings would go through with DHS 
counsel, with DHS policy. So there is a lot of oversight and 
coordination.
    Mrs. Watson Coleman. It is my understanding though that 
there is no centralized body within the Department that gives 
the program a stamp of approval or certifies that they are 
ready for prime time. Is that correct?
    Has CRCL approved your program? Do you know?
    Mr. Wagner. No. They would not necessarily go to them for 
approval, but there is----
    Mrs. Watson Coleman. Well, for approval in the sense of 
maintaining or protecting privacy rights.
    Mr. Wagner. So things are reviewed by them, and I will 
refer to my colleague.
    Mrs. Watson Coleman. What authority do you have, sir, Mr. 
Mina?
    Mr. Mina. Why do I not answer that in a couple of different 
ways, Congresswoman?
    So let me step back a second and talk a little bit about 
the first part of your question regarding sort-of the 
enterprise-level review.
    I think one of the ways in which CRCL participates in that 
dialog is by serving on enterprise level-wide working groups 
across the Department that include representatives from CDP, 
DHS S&T, and the Office of Biometric Identity Management, where 
we actually are talking about a lot of these issues.
    Now, we do not have a privacy impact assessment type model. 
However, we do work very closely with the Privacy Office 
regarding not just facial recognition technology but certainly 
other forms of biometric identification that the Department 
uses.
    Now, with regard to our relationship with CBP, we work with 
them in a couple of different ways. First is very, you know, 
directly in terms of offering them advice and then also we on-
site visits and we also work with CBP and the Privacy and Civil 
Liberties Oversight Board and their engagement as well.
    Mrs. Watson Coleman. So is your role anything more than 
just advice, observation and advice?
    You have no authority to say, ``No, that is not working. 
That is a violation.'' No, that is it, right? Advice?
    Mr. Mina. That is not entirely accurate. What I would say 
is, yes, we do have an advisory capacity. We also have a 
compliance function where we do offer recommendations to 
components based on the----
    Mrs. Watson Coleman. If they do not follow them?
    Mr. Mina. Then we can elevate it if necessary.
    Mrs. Watson Coleman. OK. I have one last question?
    Chairman Thompson. Yes.
    Mrs. Watson Coleman. The question has to do with just the 
whole system that is used when we are taking pictures and you 
know.
    Who is in charge of determining whether or not the lighting 
is good, the background is adequate, the cameras are good, they 
are placed right so that we can get the best pictures that we 
need to get?
    Is there anyone in charge of that?
    Mr. Wagner. CBP would be, and that is going to be based on, 
you know, our results of, say, the match rates. You know, you 
can have an airport with a bank of booths and the windows are 
such that the sunlight comes in and affects these booths during 
the morning and these booths in the afternoon. Those are the 
things we have got to look at as we deploy this.
    What are the environmental factors that are going to 
influence all the different locations that we are going to do 
this?
    Then we try to adjust, and that might mean we add more tint 
to the windows.
    Mrs. Watson Coleman. How do you do that? How do you do 
that?
    Mr. Wagner. We do that internally by reviewing the data and 
the results of what happens.
    Mrs. Watson Coleman. On what kind of a basis? Weekly? 
Daily? Monthly? Whatever.
    Mr. Wagner. All of it.
    Mrs. Watson Coleman. You know what time the sun comes in 
that window, and you know what time the sun comes in that 
window.
    Mr. Wagner. Right. I would say we do it continuously to get 
the best production we can out of it.
    Mrs. Watson Coleman. Thank you. Thank you very much.
    Thank you, Mr. Chairman.
    Chairman Thompson. Thank you very much.
    The Chair recognizes the Ranking Member.
    Mr. Rogers. Thank you, Mr. Chairman.
    I have a unanimous consent request that two articles on 
this topic be admitted to the record.
    Chairman Thompson. Without objection, so ordered.
    [The information follows:]
              White Paper by Security Industry Association
     what nist data shows about facial recognition and demographics
By: Jake Parker, Senior Director of Government Relations, Security 
        Industry Association, [email protected]
                              introduction
    In December 2019, the National Institute of Standards and 
Technology (NIST) published the most comprehensive report\1\ to date on 
the performance of facial recognition algorithms--the core component of 
facial recognition technology--across race, gender, and other 
demographic groups. The most significant takeaway from the NIST report 
is that it confirms current facial recognition technology performs far 
more effectively across racial and other demographic groups than had 
been widely reported; however, we've seen some misleading conclusions 
drawn from the highly technical 1,500-page report. A closer look at the 
findings in their proper context is essential to understanding the 
implications.
---------------------------------------------------------------------------
    \1\ Patrick Grether, Mei Ngan, and Kayee Hanaoka, Face Recognition 
Vendor Test (FRVT) Part 3: Demographic Effects (Washington, DC: 
National Institute of Standards and Technology, December 2019), https:/
/nvlpubs.nist.gov/nistpubs/ir/2019/NIST.IR.8280.pdf#page=69.
---------------------------------------------------------------------------
                             key takeaways
   Facial recognition technology performs far more effectively 
        across racial and other demographic groups than widely 
        reported.
   The most accurate technologies displayed ``undetectable'' 
        differences between demographic groups, calling into question 
        claims of inherent bias.
   Key U.S. Government programs are using the most accurate 
        technologies.
   Accuracy rates should always be considered in application-
        specific contexts.
             role of nist in facial recognition evaluation
    For the past 20 years, NIST's Face Recognition Vendor Test (FRVT) 
program has been the world's most respected evaluator of facial 
recognition algorithms--examining technologies voluntarily provided by 
developers for independent testing. NIST's December report is the most 
comprehensive scientific evaluation to date of client facial 
recognition technology performance across demographic variables, 
involving 189 algorithms from 99 developers using 18 million images of 
8 million people within 4 different data sets. The results are a 
snapshot in time, providing a critical benchmark against which 
developers work to improve the technology, as industry progress is 
tracked through the on-going FRVT program.
Purpose of the Report and What it Found
    NIST's report addresses ``assertions that demographic dependencies 
could lead to accuracy variations and potential bias''\2\ as well as 
flaws in prior research and media reporting. ``Much of the discussion 
of face recognition bias in recent years cites two studies showing poor 
accuracy of face gender classification algorithms on black women. Those 
studies did not evaluate face recognition algorithms, yet the results 
have been widely cited to indict their accuracy,'' according to the 
report.\3\ The most-cited figure from those papers is that 2 such 
algorithms assigned the wrong gender to photos from that demographic 
group nearly 35 percent of the time. This was reported widely in media 
reports as a groundbreaking discovery on facial recognition accuracy 
even though it did not even assess this technology.
---------------------------------------------------------------------------
    \2\ See Demographic Effects, pg. 1.
    \3\ See Demographic Effects, pg. 4.
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    In contrast, NIST found that, ``To the extent there are demographic 
differentials, they are much smaller,'' pointing out error rates in 
verification-type algorithms are ``absolutely low,'' generally below 1 
percent and many below 0.5 percent.\4\ Even more significantly, NIST 
found that in the most accurate algorithms it tested, differences in 
performance across demographic groups were ``undetectable.'' It would 
not be possible to mitigate these effects if bias is inherent in facial 
recognition technology, as some have alleged.
---------------------------------------------------------------------------
    \4\ See Demographic Effects, pg. 54.
---------------------------------------------------------------------------
    Notably for policy makers, the most well-known U.S. Government 
applications already use some of the highest-performing technologies. 
The report specifically identifies 6 suppliers of identification-type 
algorithms with undetectable differences in ``false positive'' 
rates.\5\ Included among these are current technology suppliers to the 
Federal Bureau of Investigation Criminal Justice Information Services 
Division and U.S. Customs and Border Protection's Traveler Verification 
Service.
---------------------------------------------------------------------------
    \5\ See Demographic Effects, pg. 8.
---------------------------------------------------------------------------
    For the rest of the algorithms, the report found that higher 
overall accuracy means smaller differences in performance across 
demographic groups. NIST did find relatively higher false positive 
effects for some groups in the majority of algorithms tested--depending 
on the specific metric, type of algorithm, chosen similarity score 
threshold and data set involved. However, as one recent analysis of the 
report noted ``Algorithms can have different error rates for different 
demographics but still be highly accurate.''\6\
---------------------------------------------------------------------------
    \6\ Michael McLaughlin and Daniel Castro, ``The Critics Were Wrong: 
NIST Data Shows the Best Facial Recognition Algorithms are Neither 
Racist Nor Sexist,'' Information Technology and Innovation Foundation, 
Jan. 27, 2020, pg. 3, https://itif.org/publications/2020/01/27/critics-
were-wrong-nist-data-shows-best-facial-recognition-algorithms.
---------------------------------------------------------------------------
    NIST charts comparisons across demographic groupings on a 
logarithmic scale because this granularity allows us to better perceive 
relative differences between error rates produced by algorithms that 
may be highly accurate in absolute terms. According to NIST, ``readers 
don't perceive differences in numbers near 100 percent well,'' due to 
the ``high nineties effect where numbers close to 100 are perceived 
indifferently.''\7\
---------------------------------------------------------------------------
    \7\ See Demographic Effects, pg. 22.
---------------------------------------------------------------------------
    As a result, some figures in the report appear large if considered 
only in relative terms. Using photos from over 24 countries in 7 
distinct global regions, verification-type algorithms produced false 
match rates for photos of individuals originally from East Africa as 
much as ``100 times greater than baseline.'' Although performance 
variations across demographic groups are important to continually 
assess and critically examine, outside of Somalia nearly all country-
to-country comparisons across algorithms yielded false match rates of 
less than 1 percent \8\ despite the magnitude of differences 
identified.
---------------------------------------------------------------------------
    \8\ See Demographic Effects, Annex 7.
---------------------------------------------------------------------------
    Similarly, only 4 out of 116 algoritluns tested using the U.S. 
Mugshot Identification Database had false match rates of more than 1 
percent for any demographic: Male, female, black, white, Asian, or 
American Indian.\9\ One example cited by NIST produced a 0.025 percent 
false match rate for black males and a 0.1 percent false match rate for 
black women.\10\ Compared to the rate for white males, this is 10 times 
higher for black women and 2.5 times higher for black males; however, 
these error rates are at or below \1/10\ of 1 percent.
---------------------------------------------------------------------------
    \9\ See Demographic Effects, Annex 6.
    \10\ See Demographic Effects, pg. 46, figure 12, imperial--002.
---------------------------------------------------------------------------
    Certainly, significant gaps were found between the very highest- 
and lowest-performing algorithms. NIST tests any algorithm submitted 
and many of these are in the early stages of development. Lower-
performing technologies are less likely to be deployed in commercial 
products.
                          accuracy in context
    There will always be error rates for any biometric, or any 
technology for that matter. For example, this is why NIST compared 
false match rates for different demographic groups to each other, not 
zero. How is accuracy defined when it comes to demographic effects? 
According to NIST, it means these rates ``do not vary (much) over any 
demographics.''\11\
---------------------------------------------------------------------------
    \11\ See Demographic Effects, pg. 74.
---------------------------------------------------------------------------
    Overall, modern facial recognition technology is highly accurate. 
It is in fact image quality variations like pose, illumination, and 
expression have been the primary driver of errors in facial recognition 
performance, not demographic effects, and growing immunity to such 
problems is, according to NIST, the ``fundamental reason why accuracy 
has improved since 2013.''\12\
---------------------------------------------------------------------------
    \12\ Patrick Grother, Mei Ngan and Kayee Hanaoka, Face Recognition 
Vendor Test (FRVT) Part 2: Identification (Washington, DC: National 
Institute of Standards and Technology, September 2019), pg. 8, https://
www.nist.gov/system/files/documents/2019/09/11/nistir_8271_- 
20190911.pdf.
---------------------------------------------------------------------------
    NIST has documented massive improvements in recent years, noting in 
2018 \13\ the software tested was at least 20 times more accurate than 
it was in 2014, and in 2019 \14\ finding ``close to perfect'' 
performance by high-performing algorithms with miss rates averaging 0.1 
percent. On this measurement, the accuracy of facial recognition is 
reaching that of automated fingerprint comparison, which is generally 
viewed as the gold standard for identification.\15\
---------------------------------------------------------------------------
    \13\ NIST Evaluation Shows Advance in Face Recognition Software's 
Capabilities, (Washington, DC: National Institute of Standards and 
Technology, November 2018), https://www.nist.gov/news-events/news/2018/
11/nist-evaluation-shows-advance-face-recognition-softwares-
capabilities.
    \14\ Patrick Grother, Mei Ngan, and Kayee Hanaoka, Face Recognition 
Vendor Test (FRVT) Part 2: Identification (Washington, DC: National 
Institute of Standards and Technology, September 2019), pg. 6, https://
www.nist.gov/system/files/documents/2019/09/11/nistir_8271- 
_20190911.pdf.
    \15\ See NIST's most recent fingerprint vendor technology 
evaluation of the most accurate submissions for ten finger (rolled-to-
rolled) samples, https://nvlpubs.nist.gov/nistpubs/ir/2014/
NIST.IR.8034.pdf.
---------------------------------------------------------------------------
                        lab tests vs. real-world
    We simply aren't seeing instances in the United States where 
demographic performance differences in widely-used algorithms are 
affecting facial recognition systems in high-risk settings. There are 
several reasons that may explain why.
    Algorithms comprise just one of several components of facial 
recognition systems. A human analyst will play a critical role in use 
of facial recognition as a tool in law enforcement investigations or as 
part of any process with potential high-consequence outcomes for 
individuals. There are no automated decisions made solely by the 
technology in these cases. Personnel adjudicates in situations where 
the technology may not work as well as intended. NIST has documented 
that the most accurate identification results occur when facial 
recognition is combined with trained human review, versus either 
element alone.\16\ This may explain U.S. law enforcement's decade-plus 
operating history without any example of it contributing to a mistaken 
arrest or imprisonment.
---------------------------------------------------------------------------
    \16\ NIST Study Shows Face Recognition Experts Perform Better With 
AI as Partner, (Washington, DC: National Institute of Standards and 
Technology, May 2018), https://www.nist.gov/news-events/news/2018/05/
nist-study-shows-face-recognition-experts-perform-better-ai-partner.
---------------------------------------------------------------------------
    False positives are naturally limited by the size of the data set 
used. A larger set of photos likely has a larger number of similar 
people in it; however, for many applications, the data sets are 
relatively small--the 250 passengers on a flight or 2 dozen people 
authorized to enter a building, for example, which will naturally limit 
false positives.
    NIST calls for considering different accuracy measurements within 
the context of the ``performance metric of interest'' for specific 
applications, noting the study is the first to ``properly report and 
distinguish between false positive and false negative effects.''\17\ 
The real-world implications of each depend entirely upon the specific 
use and mitigating factors. An error could be mostly inconsequential in 
cases where a ``subject experiencing a false rejection could make a 
second attempt at recognition''\18\ in order to unlock a door or device 
or clear passport control, for example.
---------------------------------------------------------------------------
    \17\ See Demographic Effects. pg. 18.
    \18\ See Demographic Effects, pg. 58.
---------------------------------------------------------------------------
    One of the report's key findings was that false positive rates vary 
much more across demographic groups than false negative effects; 
however, false negative effects are more critical to many uses 
identified.\19\ For example, facial recognition is used to detect fraud 
attempts when the same person applies for driver's license applications 
under different identities, ensuring this person is not the same as any 
other in a database. This is also how it works in many security 
applications, where the purpose of photo comparison is to ensure 
persons entering a building do not match those on a persons of interest 
list. In both cases, the false negative rate is the key performance 
measurement because the antifraud or security objective requires a very 
low likelihood of missing a possible match to flag for human review.
---------------------------------------------------------------------------
    \19\ See Demographic Effects, charts on pgs. 29, 62.
---------------------------------------------------------------------------
    For law enforcement investigations, ensuring that possible matches 
are not missed is even more critical. According to the NIST report, 
``false positive differentials from the algorithm are immaterial'' for 
law enforcement investigations since all searches produce a fixed 
number of candidates for human review regardless of any threshold for 
similarity score.\20\ On the other hand, at a port of entry, there may 
be a relatively high risk of persons attempting to enter under another 
identity, so false positive effects may be more critical. In a low-risk 
application like entry to an amusement park, both accuracy measurements 
may be less critical due to the low probability of someone trying to 
impersonate someone with a ticket and the operational need to speed 
entry by limiting rejections.
---------------------------------------------------------------------------
    \20\ See Demographic Effects, pg. 5.
---------------------------------------------------------------------------
                       limitations of the report
    Despite taking the most comprehensive look so far at demographic 
effects in facial recognition performance, the NIST report does have 
limitations and raises some unanswered questions. Most significantly, 
it is not clear whether ethnicity was fully isolated from other 
demographics or capture conditions in many instances. For example, 
false match rates for Somalia are very significant outliers that are 
not fully explained. These error rates are far higher for Somalians 
than neighboring countries in nearly every algorithm tested. For 
example, one of the most accurate verification algorithms overall had a 
false match rate of about 1 percent for Somalia, while for neighboring 
Ethiopia--which has a closely related ethnic majority--it was just 0.07 
percent, more than 14 times lower.\21\ This dramatic difference would 
suggest that the impact of ethnicity was not isolated and that other 
differences, in capture conditions, data labeling errors, etc. between 
country data exist.
---------------------------------------------------------------------------
    \21\ See Demographic Effects, Annex 7, pg. 226, tevian-005.
---------------------------------------------------------------------------
                 implications for the security industry
    Applied to security solutions developed by our industry, biometric 
technologies like facial recognition increase the effectiveness of 
safety and security measures that protect people from harm. Any 
significant bias in technology performance makes it harder to achieve 
this goal.
    We understand that there are legitimate concerns that use of facial 
recognition technology might negatively impact women and minorities. 
Industry is striving to provide technology that is as effective and 
accurate as possible across all types of uses, deployment settings and 
demographic characteristics in order to fully address these concerns.
    Both developers and end-users have a responsibility to minimize any 
negative effects that could result when the technology does not perform 
as intended though proper design, configuration, policies, and 
procedures. We strongly believe that facial recognition makes our 
country safer and brings value to our everyday lives when used 
effectively and responsibly. No technology product should ever be used 
for purposes that are unlawful, unethical, or discriminatory.
 FACE FACTS: HOW FACIAL RECOGNITION MAKES US SAFER & THE DANGERS OF A 
                              BLANKET BAN
    Facial recognition technology makes our country safer and brings 
value to our everyday lives when used effectively and responsibly. The 
Security Industry Association (SIA) believes all technology products, 
including facial recognition technology, must only be used for purposes 
that are lawful, ethical, and nondiscriminatory.
   Modern facial recognition technology is highly accurate. The 
        National Institute of Standards and Technology (NIST) found 
        that the facial recognition software it tests is now over 20 
        times better than it was in 2014 at searching a database to 
        find a matching photograph. NIST's September 2019 report found 
        ``close to perfect'' performance by high-performing algorithms 
        with miss rates averaging 0.1 percent, reaching the accuracy of 
        fingerprint comparison technology--the gold standard for 
        identification.
   The benefits of facial recognition have been proven for more 
        than a decade of use in real-world applications, including 
        finding missing and exploited children, protecting critical 
        infrastructure, and aiding law enforcement investigations. See 
        examples of the benefits in action on the reverse page.
                why a blanket ban puts americans at risk
   A blanket ban on Government use precludes all possible 
        current and future applications of the technology, regardless 
        of the purpose, putting the safety of every resident at risk.
   Beyond law enforcement, such a ban prohibits other proven 
        uses like secured employee access to critical infrastructure 
        and other systems that protect building occupants and software 
        that detects fraud against Government programs, to name a few.
   Such bans have also been defined broadly, prohibiting any 
        Government official, employee, contractor or vendor from using 
        any technology with facial recognition capabilities, including 
        social media platforms and smartphones.
   A ban on facial recognition eliminates a useful tool that is 
        being used alongside human intelligence. Thorough analysis must 
        acknowledge the alternatives a ban would leave us with--far 
        slower and less accurate identification processes chat are much 
        more prone to errors (for example, detectives sifting manually 
        through hundreds or even thousands of videos and images of 
        arrested individuals based on suspect descriptions). NIST 
        confirmed in a 2018 study chat the highest identification 
        accuracy is achieved through human analysis supported by facial 
        recognition technology versus either element alone.
   Before taking such an extreme step, policy makers must 
        thoroughly examine how the technology is used and consider all 
        the options available to address concerns. Sensible 
        transparency and accountability measures can be identified that 
        would ensure responsible use of the technology without 
        unreasonably restricting cools chat have become so essential to 
        public safety.
                   FACE FACTS: KEEPING AMERICANS SAFE
    SAVING SEX TRAFFICKING VICTIMS.--In April 2019, a California law 
enforcement officer saw a social media post about a missing child from 
the National Center for Missing and Exploited Children. The officer 
used facial recognition which returned a list of on-line sex ads 
featuring the girl.
    According to a story in WIRED, the girl had been ``sold for 
weeks,'' and the officer's actions helped a process that ``recovered 
and removed from the girl from trauma.''
    CATCHING A NEW YORK CITY SUBWAY TERRORIST.--In August 2019, New 
York Police Department detectives used facial recognition to help 
identify a man who sparked terror by leaving rice cookers in and around 
a subway station. Detectives pulled still images from security footage 
and used facial recognition software, along with additional 
investigative work, to identify the suspect within an hour: NYPD 
officials were quoted saying, ``To not use technology like this would 
be negligent'' and ``This is the most important type of case that we'd 
see out there: a possible terrorist attack in NYC.''
    FINDING A KILLER WHO TARGETED LGBTQ VICTIMS.--On May 25, 2019, in 
Wayne County, Michigan, 3 members of the LGBTQ community were shot and 
killed by a man at a gas station. The Detroit Police Department used 
facial recognition, as well as their own intelligence, to help identify 
the suspect, who was charged with 3 counts of murder in addition to 
other charges.
    IDENTIFYING THE CAPITAL GAZETTE KILLER.--Jarrod Ramos was angered 
by a story the Capital Gazette Newspaper in Annapolis, Maryland, ran 
about him in 2011 and brought a lawsuit against the paper for 
defamation, which a judge later dismissed. In June 2018, Ramos entered 
the newspaper building with a shotgun and killed 5 employees, leaving 2 
others critically injured. Anne Arundel Police obtained an image of 
Ramos and sent it to the Maryland Combined Analysis Center, which 
helped identify him by comparing the photo to others in the Maryland 
Image Repository System.
    APPREHENDING PEDOPHILES EVADING JUSTICE.--In 2017, after a 16-year 
manhunt, a man accused of sexually assaulting a minor was apprehended 
in Oregon. Using facial recognition technology, the Federal Bureau of 
Investigation (FBI) was able to identify the suspect after a positive 
match was found when the suspect sought to acquire a U.S. passport. 
Similarly, in 2014, the FBI used facial recognition technology to help 
locate and apprehend a convicted pedophile who had been on the run for 
14 years.
    PREVENTING ENTRY INTO THE UNITED STATES UNDER FALSE IDENTITIES.--
After just 3 days of operation, facial recognition technology at Dulles 
International Airport in Virginia caught a man trying to use a fake 
passport to enter the United States. The fraudulent passport would have 
easily gone undetected with visual inspection alone. The ability to 
enter under a false identity is essential to organized crime, human 
trafficking, money laundering, drug smuggling, terrorism, and many 
other criminal activities. According to U.S. Customs & Border 
Protection, use of the technology prevented 26 alleged imposters from 
entering the United States in just a 3-month span in 2018.
                                 ______
                                 
   Article From the Information Technology and Innovation Foundation 
                                 (ITIF)
  The Critics Were Wrong: NIST Data Shows the Best Facial Recognition 
                Algorithms Are Neither Racist Nor Sexist
By: Michael McLaughlin and Daniel Castro/January 2020
    A close look at data from a new NIST report reveals that the best 
facial recognition algorithms in the world are highly accurate and have 
vanishingly small differences in their rates of false positive or 
false-negative readings across demographic groups.
                              introduction
    The National Institute of Standards and Technology (NIST) recently 
released a report that examined the accuracy of facial recognition 
algorithms across different demographic groups. The NIST report found 
that the most accurate algorithms were highly accurate across all 
demographic groups. But NIST tested nearly 200 algorithms from vendors 
and labs around the world--it allows anyone to submit an algorithm for 
testing--and since many of the algorithms it tested displayed some 
bias, several news outlets and activists have misleadingly concluded 
that facial recognition systems are racist and sexist.\1\ But a close 
look at the data reveals a different picture.
---------------------------------------------------------------------------
    \1\ Tom Higgins `` `Racist and Sexist' Facial Recognition Cameras 
Could Lead to False Arrests,'' The Telegraph, December 20, 2019, 
https://www.telegraph.co.uk/technology/2019/12/20/racist-sexist-facial-
recognition-cameras-could-lead-false-arrests.
---------------------------------------------------------------------------
    Facial recognition technology compares images of faces to determine 
their similarity, which the technology represents using a similarity 
score. The technology often performs one of two types of comparisons. 
The first comparison is known as a one-to-many or identification 
search, in which the technology uses a probe image to search a database 
of images to find potential matches. The second comparison is known as 
a one-to-one or verification search as the technology compares 2 images 
to determine the similarity of the faces in them. In many cases, the 
faces in images are considered a match if their similarity score meets 
or exceeds the match threshold, a number the operator assigns that 
represents a minimum acceptable similarity score. The technology has 
many commercial and non-commercial uses, and will likely be integrated 
into more products and services in the future to enhance security, 
improve convenience, and increase efficiency. such as by helping find 
victims of human trafficking. expediting passengers through airport 
security, and flagging individuals using forged identification.\2\
---------------------------------------------------------------------------
    \2\ Tom Simonite, ``How Facial Recognition Is Fighting Child Sex 
Trafficking,'' Wired, June 19 2019, https://www.wired.com/story/how-
facial-recognition-fighting-child-sex-trafficking/; ``Face Recognition 
Nabs Fake Passport User at US Airport,'' VOA News, August 24, 2018 
https://www.voanews.com/silicon-valley-technology/face-recognition-
nabs-fake-passport-user-us-airport.
---------------------------------------------------------------------------
    NIST assessed the false positive and false-negative rates of 
algorithms using 4 types of images, including mugshots, application 
photographs from individuals applying for immigration benefits, visa 
photographs, and images taken of travelers entering the United States. 
NIST's report reveals that:
   The most accurate identification algorithms have `` 
        undetectable'' differences between demographic groups;\3\
---------------------------------------------------------------------------
    \3\ We defined the most accurate identification algorithms as the 
20 algorithms that had the lowest false-negative identification rates 
for placing the correct individual at rank one when searching a 
database that had images of 12 million individuals in NIST's September 
2019 identification report. NIST provided error characteristics data by 
race and sex for 10 of these algorithms in its recent report. 
Consequently, we analyzed the performance of NEC-2, NEC-3, Visionlabs-7 
, Microsoft-5, Yitu-5, Microsoft-0, Cogent-3, ISystems-3, 
NeuroTechnology-5, and NTechlab-6; Patrick Grother, Mei Ngan, and Kayee 
Hanaoka, Face Recognition Vendor Test (FRVT) Part 2: Identification 
(Washington, DC: National Institute of Standards and Technology, 
September 2019), 47 https://www.nist.gov/system/files/documents/2019/
09/11/nistir_8271_20190911.pdf#page=49.
---------------------------------------------------------------------------
   The most accurate verification algorithms have low false 
        positives and false negatives across most demographic 
        groups;\4\
---------------------------------------------------------------------------
    \4\ We defined the most accurate verification algorithms as those 
that rank in the top 20 on NIST's FRVT 1:1 leaderboard on January 6, 
2020. NIST has since updated the leaderboard. Not all of these 
algorithms were tested in NIST's most recent demographics report. We 
analyzed the performance of algorithms that NIST provided data for in 
Annexes 6, 13, 15, and Figure 22. These algorithms are visionlabs-007, 
everai-paravision-003, didiglobalface-001, imperial-002, dahua-003, 
tevian-005, alphaface-001, ntechlab-007, yitu-003, innovatrics-006, 
facesoft-000, intellifusion-001, anke-004, hik-001, camvi-004, vocord-
007, and tech5-003; National Institute of Standards and Technology, 
FRVT 1:1 Verification (FRVT 1: 1 leaderboard, accessed January 6, 
2020), https://pages.nist.gov/frvt/html/frvt11.html.
---------------------------------------------------------------------------
   Algorithms can have different error rates for different 
        demographics but still be highly accurate.
                              key findings
    As detailed below, NIST found that the most accurate algorithms--
which should be the only algorithms used in Government systems--did not 
display a significant demographic bias. For example, 17 of the highest-
performing verification algorithms had similar levels of accuracy for 
black females and white males: False-negative rates of 0.49 percent or 
less for black females (equivalent to an error rate of less than 1 in 
200) and 0.85 percent or less for white males (equivalent to an error 
rate of less than 1.7 in 200).\5\
---------------------------------------------------------------------------
    \5\ The high-performing algorithms include visionlabs-007, everai-
paravision-003, didiglobalface-001, imperial-002, dahua-003, tevian-
005, alphaface-001, ntechlab-007, yitu-003, innovatrics-006, facesoft-
000, intellifusion-001, anke-004, hik-001, camvi-004, vocord-007, and 
tech5-003. Comparisons made at the same match threshold.
---------------------------------------------------------------------------
    While the most accurate algorithms did not display a significant 
demographic bias, it is also true that the majority of the algorithms 
NIST tested generally performed better on men and individuals with 
lighter skin tones. However, it is important to recognize that there is 
a stark difference between the best and worst algorithms. In comparison 
to the false-negative rates under 1 percent for black females and white 
males among the highest-performing algorithms, the lowest-performing 
algorithms had false-negatives rates, for blacks and whites, as high as 
99 percent.\6\ This wide range of accuracy is not surprising 
considering that NIST allows anyone to submit an algorithm for testing, 
ranging from large companies with production systems to small research 
groups whose algorithms have not left the lab--algorithms are tested 
even if they are not incorporated into a commercially-available 
product.
---------------------------------------------------------------------------
    \6\ The low-performing algorithms, according to their performance 
for false non-match rate on Figure 22 of the NIST demographics report, 
include shaman-001, isap-001, ayonix-000, amplifiedgroup-001, saffe-
001, videonetics-001, and chtface-001.
---------------------------------------------------------------------------
The Most Accurate Identification Algorithms Have Undetectable 
        Differences Between Demographics
    NIST found that some highly-accurate algorithms had false-positive 
demographic differentials that were so small as to be ``undetectable'' 
for one-to-many searches.\7\ Moreover, for most algorithms, black men 
had lower false-negative rates than white men, and several of the top 
algorithms had better false-negative rates for white women than white 
men.\8\ Several algorithms also provided uniform similarity scores 
across demographic groups, meaning that the algorithms provided similar 
match and non-match scores regardless of race and gender.\9\ The 
uniform scores indicate that these algorithms would have small 
demographic differentials if an operator applied a threshold. But 
different thresholds can affect demographic differentials. For example, 
at least 6 of the most accurate identification algorithms had higher 
false-positive rates for black men than white men at one threshold, but 
lower false-positive rates for black men than white men at another 
threshold.\10\
---------------------------------------------------------------------------
    \7\ Patrick Grether, Mei Ngan, and Kayee Hanaoka, Face Recognition 
Vendor Test (FRVT) Part 3: Demographic Effects (Washington, DC: 
National Institute of Standards and Technology, December 2019), 3, 
https://nvlpubs.nist.gov/nistpubs/ir/2019/NIST.IR.8280.pdf#page=6.
    \8\ To compare white men and white women, we analyzed false-
negative rates for ranking the correct matching image as the top 
potential match. Algorithms that had lower false-negative rates for 
white women than white men include NEC-2, NEC-3, and Visionlabs-7; 
Patrick Grether, Mei Ngan, and Kayee Hanaoka, Face Recognition Vendor 
Test(FRVT) Part 3: Demographic Effects (Washington, DC: National 
Institute of Standards and Technology, December 2019), 63 https://
nvlpubs.nist.gov/nistpubs/ir/2019/NIST.IR.8280.pdf#page=66; National 
Institute of Standards and Technology, On-going Face Recognition Vendor 
Test (FRVT) (part 3: demographic effects, annex 16: identification 
error characteristics by race and sex), https://pages.nist.gov/frvt/
reports/demographics/annexes/annex_16.pdf.
    \9\ Patrick Grether, Mei Ngan, and Kayee Hanaoka, Face Recognition 
Vendor Test (FRVT) Part 3: Demographic Effects (Washington, DC: 
National Institute of Standards and Technology, December 2019), 66, 
https://nvlpubs.nist.gov/nistpubs/ir/2019/NIST.IR.8280.pdf#page=69.
    \10\ These algorithms include Visionlabs-7, Microsoft-5, Yitu-5, 
Microsoft-0, ISystems-3, and NeuroTechnology-5; National Institute of 
Standards and Technology, On-going Face Recognition Vendor Test (FRVT) 
(part 3: demographic effects, annex 16: identification error 
characteristics by race and sex), https://pages.nist.gov/frvt/reports/
demographics/annexes/annex_16.pdf.
---------------------------------------------------------------------------
The Most Accurate Verification Algorithms Have Low False Positives and 
        Negatives Across Most Demographics
    The most accurate verification algorithms have low false positives 
and negatives across most demographics. For example, when NIST applied 
thresholds so that the algorithms had false positive rates of 0.01 
percent for white males, more than half of the 17 most accurate 
algorithms had false-positive rates of 0.03 percent or better for black 
males, Asian men, and white women.\11\ This equates to the algorithms 
falsely matching these individuals 3 times or less per every 10,000 
comparisons to an imposter compared to 1 per every 10,000 for white 
males. At another threshold, 7 of the top algorithms displayed no 
false-positive bias between white men, black men, Asian men, and white 
females.\12\ At this threshold, several algorithms also had false-
positive rates of 0.003 percent or less for black women or Asian women 
while white males had false-positive rates of 0.001 percent.\13\
---------------------------------------------------------------------------
    \11\ One of these algorithms, for example, is visionlabs-007; 
National Institute of Standards and Technology, On-going Face 
Recognition Vendor Test (FRVT) (part 3: demographic effects, annex 6: 
cross-race and sex false match rates in United States mugshot images), 
https://pages.nist.gov/frvt/reports/demographics/annexes/annex_06.pdf.
    \12\ An example of such an algorithm is anke-004.
    \13\ An example of such an algorithm is yitu-003.
---------------------------------------------------------------------------
    False negatives were also low for the most accurate verification 
algorithms. Five of the 17 most accurate algorithms had false-negative 
rates of less than 1 percent across all demographic groups when NIST 
applied a threshold that set false-positive rates at 0.01 percent.\14\ 
Similarly, the best verification algorithms had less than 1 percent 
false-negative rates across countries and demographic groups. For 
example, the algorithm Visionlabs-007 had below a 1 percent false-
negative rate for nearly all countries and demographic groups for 
border crossing application images. There were two exceptions--Somalian 
and Liberian women under 45. Nonetheless, the algorithm had a false-
negative rate below 1.4 percent for each of these groups.
---------------------------------------------------------------------------
    \14\ These algorithms are visionlabs-007, everai-paravision-003, 
didiglobalface-001, alphaface 001, and intellifusion-001; National 
Institute of Standards and Technology, On-going Face Recognition Vendor 
Test (FRVT) (part 3: demographic effects, annex 15: genuine and 
imposter score distributions for United States mugshots), https://
pages.nist.gov/frvt/reports/demographics/annexes/an nex_15.pdf.
---------------------------------------------------------------------------
Algorithms Can Have Different Error Rates for Different Demographics 
        But Still Be Highly Accurate
    Some algorithms perform differently on one group compared to 
another, but still maintain true positive and true negative accuracy 
rates greater than 99 percent for all races and sexes.\15\ Because 
these algorithms have very low error rates, differences that are small 
in absolute terms may seem large if expressed in relative terms. For 
example, an algorithm from Dutch firm VisionLabs, Visionlabs-007, had a 
false-negative rate 4 times higher for the nationality it performed 
poorest on (Somalian) than the nationality it performed best on 
(Salvadoran).\16\ Nonetheless, the algorithm only had a false-negative 
rate of 0.63 percent for individuals from Somalia. Another example is 
the performance difference of a verification algorithm from Camvi, a 
firm based in Silicon Valley, for white males and American Indian 
females. At one particular threshold, the algorithm had a false-
positive rate that was 13 times higher for American Indian females than 
white men.\17\ But at this threshold, the algorithm had barely more 
than 1 false match of American Indian females for every 10,000 imposter 
comparisons to other American Indian females. It is also true that most 
verification algorithms had higher false-negative rates for women than 
men. But NIST notes that this ``is a marginal effect--perhaps 98 
percent of women are still correctly verified--so the effect is 
confined to fewer than 2 percent of comparisons where algorithms fail 
to verify.''
---------------------------------------------------------------------------
    \15\ These algorithms include visionlabs-007 and everai-paravision-
003.
    \16\ In this case, NIST set the threshold to ``the lowest value 
that gives FMR less than or equal to 0.00001.''
    \17\ National Institute of Standards and Technology, On-going Face 
Recognition Vendor Test (FRVT) (part 3: demographic effects, annex 15: 
genuine and imposter score distributions for United States mugshots, 
19), https://pages.nist.gov/frvt/reports/demographics/annexes/
annex_15.pdf#20.
---------------------------------------------------------------------------
                     putting nist's data in context
    Recent reporting on how law enforcement in San Diego used facial 
recognition from 2012-2019 can also help put NIST's data in context. In 
2018, various law enforcement entities made 25,102 queries to a 
database of 1.8 million mugshot images.\18\ Law enforcement officials 
uses of the technology included attempts to determine whether an 
individual had a criminal record and attempts to discover the identity 
of individuals who lacked identification. These use cases were likely 
one-to-many searches. Law enforcement did not track the success of the 
program, making it unclear how many false positives or false negatives 
the system registered as well as how many mated or non-mated searches--
a search in which an image of the individual was not in San Diego's 
database--they performed.
---------------------------------------------------------------------------
    \18\ DJ Pangburn, ``San Diego's Massive, 7-Year Experiment With 
Facial Recognition Technology Appears to Be a Flop,'' Fast Company, 
January 9, 2020, https://www.fastcompany.com/90440198/san-diegos-
massive-7-year-experiment-with-facial-recognition-technology-appears-
to-be-a-flop.
---------------------------------------------------------------------------
    But we can consider a few scenarios to make a rough estimate of how 
the most accurate algorithms might perform in a city like San Diego, 
assuming San Diego's images and hardware were of similar quality to 
NIST's.\19\ Under the first scenario, let us assume that all 25,102 
probe images law enforcement used had a match in the database of 1.8 
million mugshot images (an unlikely event), and that law enforcement 
did not apply a threshold to limit false positives or negatives (also 
unlikely). NEC-2, the best identification algorithm NIST tested in an 
earlier 2019 report, failed to rank the correct candidate as the most 
likely match only 0.12 percent of the time when performing a search of 
a database containing images of 3 million individuals.\20\ At this 
rate, the technology would have succeeded in listing the correct 
individual in the San Diego search as the most likely match 24,970 
times out of the 25,000 searches and failed 30 times.
---------------------------------------------------------------------------
    \19\ In each of the scenarios, we are assuming that the racial and 
gender makeup of San Diego's mugshot database is similar to NIST's 
mugshot database.
    \20\ Patrick Grother, Mei Ngan, and Kayee Hanaoka, Face Recognition 
Vendor Test (FRVT) Part 2: Identification (Washington, DC: National 
Institute of Standards and Technology, September 2019), 47, https://
www.nist.gov/system/files/documents/2019/09/11/nistir_8271_- 
20190911.pdf#page=49.
---------------------------------------------------------------------------
    Under a second scenario, let us assume law enforcement applied a 
threshold that allowed for 1 false positive every 1,000 non-mate 
searches. At this rate, NEC-3 had a false-negative rate of 0.26 
percent. We also assume that half of the more than 25,000 probe images 
had a match in the database and that half did not have a match. In this 
scenario, the algorithm would have registered 13 false positives and 33 
false negatives.
                               conclusion
    Developers and users of facial recognition technology, law 
enforcement, and lawmakers can take several actions to promote the 
development and responsible use of facial recognition technology. 
First, developers should continue to improve accuracy rates across 
different demographics, including by diversifying their datasets.\21\ 
Second, the Government should set standards for the accuracy rates of 
the systems it deploys. Third, law enforcement should have standards 
for the quality of images it uses in a facial recognition search, which 
can affect the accuracy of facial recognition algorithms.\22\ Fourth, 
the users of facial recognition technology should carefully choose 
which match threshold is appropriate for their goal. Last, lawmakers 
should consider how law enforcement typically uses the technology and 
the different implications of false positive and false negatives when 
developing regulations. In most law enforcement scenarios, law 
enforcement is using facial recognition technology to return a list of 
possible suspects that humans review. And there are different 
implications when algorithms incur false positives or false negatives. 
In many cases, a subject can make a second attempt at recognition when 
a facial recognition system produces a false negative. This implication 
differs from the possible effects of false positives, which could allow 
an individual access to a facility they should not enter.
---------------------------------------------------------------------------
    \21\ Chinese developers often had lower false positives for Chinese 
faces, suggesting that increasing the representation of minority faces 
in training data may reduce bias.
    \22\ For example, although the false-negative rates were frequently 
lowest for black individuals in mugshot images, false-negative rates 
were relatively high for black individuals in images taken at border 
crossings. This difference could result from inadequate exposure in the 
latter photos; Patrick Grother, Mei Ngan, and Kayee Hanaoka, Face 
Recognition Vendor Test (FRVT) Part 3: Demographic Effects (Washington, 
DC: National Institute of Standards and Technology, December 2019), 54, 
https://nvlpubs.nist.gov/nistpubs/ir/2019/NIST.IR.8280.pdf#page=57.
---------------------------------------------------------------------------
    Finally, while there is no place for racial, gender, or other types 
of discrimination in societies, to ban facial recognition unless it 
performs exactly the same across every conceivable group is impractical 
and would limit the use of a societally valuable technology. Many 
critics of facial recognition technology complain that the technology 
is not accurate enough, but refuse to give specifics on what they would 
consider sufficient--refusing to set a clear goal post for industry--
which suggests they are not serious about wanting to improve the 
technology and oppose it for other reasons.
    Reasonable people may disagree on when it is appropriate to use 
facial recognition, but the facts are clear that the technology can be 
highly accurate. As previous NIST reports have shown, many of the 
algorithms have accuracy rates that exceed 99 percent, and as the new 
report shows, the differences across demographics are minimal for the 
best algorithms.\23\
---------------------------------------------------------------------------
    \23\ For example, a previous NIST report revealed that the top 20 
identification algorithms failed to place the correct individual as the 
top potential match when searching a database containing images of 12 
million individuals less than 1 percent of the time; Patrick Grother, 
Mei Ngan, and Kayee Hanaoka, Face Recognition Vendor Test (FRVT) Part 
2:--Identification (Washington, DC: National Institute of Standards and 
Technology, September 2019), 47, https://www.nist.gov/system/files/
documents/2019/09/11/nistir_8271_20190911.pdf#page=49.

    Mr. Rogers. Thank you, sir.
    Chairman Thompson. The Chair recognizes the gentlelady from 
Texas, Ms. Jackson Lee.
    Ms. Jackson Lee. Let me thank the Chairman very much.
    Let me acknowledge all of the Government witnesses and 
thank them for their service.
    Let me renew the inquiry that will be pursued by Chairwoman 
Rice, but I will add to it, and that is, Mr. Wagner, a better 
understanding. Maybe you will provide the information to the 
committee on the denial of clear global and Trusted Traveler as 
relates to States that may not have the laws that you think are 
appropriate or, in the instance of New York, closing out access 
to the issue of driver's license.
    I raise the question because we should look as the Federal 
Government at what other identification options may be valid.
    I know that we have known each other for a long time, and I 
would think that you would be willing to look at that so that 
we can find common ground.
    Let me pursue this line of reasoning, and please, 
witnesses, understand that I am not saying this is what you are 
doing. I need to understand your thinking.
    So to the Deputy Executive Assistant Commissioner Wagner, 
would you accept the fact that bias could be introduced by 
technology if the application developer of the program had a 
bias into how an application reacts to different types of 
people because it is technology?
    Mr. Wagner. Yes.
    Ms. Jackson Lee. I would also make the point though it is a 
little bit humorous, is I am sure the people in Iowa were 
trusting of the app and thought they had something going on 
there, and we all can see where we are at this point.
    Would you accept that, Mr. Mina?
    Mr. Mina. Yes, Congresswoman.
    Ms. Jackson Lee. Would NIST accept that, Mr. Romine?
    Mr. Romine. Yes.
    Ms. Jackson Lee. Yes. An algorithm could be--again, know 
that this is not pointed toward you--be written to flag all 
black males wearing dreadlocks.
    Mr. Wagner, this is in terms of how technology can be.
    Mr. Wagner. I guess you could.
    Ms. Jackson Lee. I understand. You can say on the record to 
your knowledge, you are not using that kind of algorithm.
    Mr. Wagner. We are not using that. I can----
    Ms. Jackson Lee. That would be very good. I am sure 
dreadlock wearers would be glad of that.
    Mr. Mina.
    Mr. Mina. Yes, that is possible, and again, as Mr. Wagner 
said, we have not seen that in our review as well.
    Ms. Jackson Lee. All right. Mr. Romine.
    Mr. Romine. It is certainly possible.
    Ms. Jackson Lee. OK. So here we are. Let me to my 
colleagues over here that are DNA advocates, as a member of the 
Judiciary Committee, which I had to step away from, we are DNA 
lovers. I wrote the Violence Against Women Act and put in $291 
million for DNA enhancement. So we understand that as the new 
added technology.
    But as the Department of Homeland Security, we made a 
commitment post-9/11 with George Bush going to the Trade and 
saying he heard the firefighters, but at the same time he also 
heard Muslims who were indicating it is not the blanket world 
of people who happen to be Muslim.
    So, in particular, Mr. Mina, I want to try to find out what 
aggressive role do you play in helping to not have platitudes. 
Forgive me. I am not suggesting you do, but to aggressively 
ensure that the biases against black women with dreadlocks, men 
with dreadlocks, Muslims or Sikhs wearing attire.
    I went through that. I have been on this committee since 
its beginning. That is not technology, but and then now 
sophisticated technology is not undermining the civil liberties 
and civil rights of this Nation and those coming in innocently 
to the country. You can use the new technology as well.
    Then to Mr. Romine, let me find out how are you continuing 
to do your assessment of these algorithms to ensure that it 
looks like you were not able to get the exact one that Mr. 
Wagner's team is using. That concerns me.
    I need you to get every accurate piece of information, and 
I would like you to say that.
    Mr. Mina, what aggressiveness are you doing to protect the 
travelers and the American people?
    Mr. Mina. I thank you for the question, Congresswoman.
    So I think we are doing a lot of different things across 
the spectrum and the life cycle of this program and policy, and 
again, I want to focus. Our attention is really on the 
application, not so much on the algorithm itself, but on how it 
is applied by particularly a DHS program, in this case, CBP.
    We do that through, on the policy-making side, working 
directly with the component, advising on proposed regulations 
of implementing policies, as well as offering suggestions as it 
relates to applications, for example, folks wearing religious 
headwear or folks that have objections to photography based on 
religious reasons or the people who are disabled or otherwise 
injured and area not able to take pictures.
    We also do it through our robust community engagement. We 
talk to members of the community across the country, and, Mr. 
Chairman, I actually have the information in front of me 
regarding some of the areas.
    It is the issues that have been raised in Portland, in 
Atlanta, in Chicago and Seattle, and then also to a lesser 
extent in Southern California, primarily L.A. and Orange 
County, and then by New York City area stakeholders where we 
have heard concerns regarding facial recognition technology.
    One of our primary roles is to be the eyes and ears of the 
Department, and we inform our colleagues at CBP, at DHS S&T, at 
OBIM. Here are the concerns that we are seeing. How do we work 
together to try and address some of these problems or potential 
problems before they have even greater effect?
    Then also, on the back end we have a robust compliance 
process, and while we do not have an active investigation right 
now on facial recognition, that is always something that we are 
looking at. If we see a trend, we will most certainly open an 
investigation and, again, advice in that way as well.
    Ms. Jackson Lee. Would you be kind enough, Mr. Chairman, to 
let Mr. Romine answer his question?
    As he answers, Mr. Chairman, I just want to say this on the 
record, if we can get answers from Mr. Wagner about what is 
stored in terms of retaining information.
    Chairman Thompson. Ms. Jackson Lee.
    Ms. Jackson Lee. Thank you. Mr.----
    Chairman Thompson. No. Dr. Romine, you can answer the 
question. You can submit in writing to Ms. Jackson Lee.
    Ms. Jackson Lee. No, that is what I am saying.
    Chairman Thompson. Yes.
    Ms. Jackson Lee. Yes. Thank you.
    Chairman Thompson. Be happy to do it.
    Ms. Jackson Lee. Thank you.
    Mr. Romine, my question was----
    Mr. Romine. I beg your pardon, ma'am?
    Ms. Jackson Lee. Yes. My question was: What are you doing 
to be accurate in your testing?
    You said you did not know whether you had the accurate app 
that they were using. What are you doing to be aggressive in 
making sure that we do not have the bias in these algorithms?
    Mr. Romine. Yes, ma'am. The tests that we undertake are 
intended to determine whether there are demographic 
differences, commonly called bias. The fact that I know there 
is strong interest in testing with data that is more 
representative, and we have signed a recent MOU with the CBP to 
undertake continued testing to make sure that we are doing the 
very best that we can to provide the information that they need 
to make sound decisions.
    Ms. Jackson Lee. Thank you very much.
    I yield back. Thank you.
    Chairman Thompson. Thank you.
    The Chair recognizes the gentleman from Texas, Mr. Green.
    Mr. Green of Texas. Thank you, Mr. Chairman.
    I thank the witnesses for appearing as well.
    I would like to address some intelligence that has been 
afforded me. The indication is that NIST found that Asian and 
African American faces were 10 times more likely, well, 10 to 
100 times more likely to be misidentified than white faces.
    I am curious as to whether or not there is something 
inherent in the technology that creates an inverse relationship 
with reference to the identification of whites juxtaposed to 
African Americans and Asians.
    Is there something inherent in the technology, meaning if 
you want to absolutely identify whites, will there be something 
that you cannot adjust such that you will get the same absolute 
identification with minorities, Asians, African Americans?
    Or if you want to absolutely identify African Americans and 
Asians, will you, as a result of technology, not be able to 
properly identify whites?
    Mr. Romine. It is a very interesting question, Congressman.
    Mr. Green of Texas. Thank you.
    Mr. Romine. Let me clarify first that those differentials 
that we observed were not in the case of identification but 
rather verification, the one-to-one testing rather than the 
one-to-many testing. In general, we saw those demographic 
differences for African Americans and for Pacific Islanders and 
Asians as well.
    But in the case that you are talking about, our work has 
not to-date focused on cause and effect. What is it that is 
causing the algorithms to exhibit certain kinds of behavior? We 
are really just testing the performance.
    So I do not know the answer to your question.
    Mr. Green of Texas. My question was interesting, as you put 
it. Your answer is intriguing because this is not the first 
opportunity for the word to be heard that we have these 
difficulties, and at some point, it would seem that we would 
move from testing technology as it is to understanding why 
technology performs the way it does.
    Help me to understand why we have not made that move?
    Mr. Romine. The question that you asked is a very 
challenging open research question, but we do have some 
indications.
    There are algorithms that have been submitted to our 
testing from Asian countries that do not exhibit the 
demographic differentials on Asian faces. So we cannot 
guarantee, but we think that is an indication that the training 
data that are being used for the algorithm development may have 
a significant impact on their ability to discern or exhibit 
demographic differences for different populations.
    Mr. Green of Texas. Do you believe that it is important for 
us to move expeditiously to answer this question, to resolve 
this issue such that we do not find ourselves having deployed 
something en masse that we know to be defective or have some 
degree of inefficiencies associated with it?
    The efficacy of this is important.
    Mr. Romine. Yes, sir, and I think those are two different 
things to think about. The performance testing that we 
currently execute can help operational agencies ensure that 
they are not deploying things that exhibit demographic 
differentials.
    The research question that you teed up that is fascinating 
about what are the causes of these demographic differentials is 
a much deeper question and much more difficult, I think.
    Mr. Green of Texas. Well, is it fair to say that the 
countries--and I have about 45 seconds left--but the countries 
that employ the technology that have indicated to you they are 
having fewer challenges, is it fair to say that that technology 
also captures white men sufficiently?
    Mr. Romine. In the testing that we did for the specific one 
that I am referring to, the high-performing algorithms from 
Asian countries that do not exhibit the demographic differences 
on Asians, it is in comparison to Caucasian faces that I made 
that statement.
    So there is no difference in the performance or discernable 
difference in the performance on Caucasian faces and Asian 
faces from certain Asian-developed algorithms, and one 
speculation is that it may be the training data that are used.
    Mr. Green of Texas. Thank you, Mr. Chairman. I yield back.
    Chairman Thompson. Thank you very much.
    The Chair recognizes the gentleman from Rhode Island for 5 
minutes. Mr. Langevin.
    Mr. Langevin. Thank you, Mr. Chairman.
    I want to thank our witnesses for your testimony here 
today, and thank you for you are doing, your dedication to this 
issue, a very important issue.
    I certainly believe that technology is an important part of 
the solutions that some of our most vexing issues and 
challenges, including how to manage an ever-growing number of 
international travelers. So it has been a good discussion here 
today.
    What I wanted to ask of either Mr. Wagner or Mr. Mina, we 
know that in technological solutions, such as facial 
recognition software, the algorithms are only as good as the 
data that inform them. So I want to know how has CBP adjusted 
or augmented the data that it uses to train its facial 
recognition software.
    What are you doing to ensure the software is continually 
updated as more robust data sets and algorithms are 
incorporated into training?
    Mr. Wagner. That is where we work closely with the vendor, 
whose algorithm we are using, NEC, and we work closely with 
them to incorporate their updates and their latest and greatest 
products into how we are using them.
    Then as we review the data, you know, we look to make those 
operational adjustments, which do impact metrics, and again, 
that is going to be the quality of the photograph, the quality 
of the camera, the human factors.
    The size of the gallery is really important, and you know, 
in this, it tested galleries up to, I think, 12 million people. 
You know, on the margins of the capabilities of these 
algorithms, we are doing this on a couple thousand, and 
interesting correlations are how much better improved is your 
match rates and what is the impact on any potential demographic 
biases on a much smaller gallery or sample size.
    I think that is what we were getting at earlier, that what 
are these variables that we can raise or lower to help address 
some of what the error rates are showing us.
    Mr. Langevin. OK. So to that point then, how does CBP 
incorporate feedback from officers about errors that facial 
recognition software has made in the field?
    Because the machine, it learns. When the officer is 
looking, interacting with someone, and the software does not 
get it correct, unless that feedback is fed back into the 
system, the system does not learn.
    Mr. Wagner. Oh, absolutely, and that is where we look at 
the system logs themselves, but we also talk to the officers. 
They provide the feedback, and then we are also on-site to 
witness and observe and discuss with those officers as we 
deploy these.
    Mr. Langevin. That is important.
    So I understand the Trusted Traveler Program shares 
information with other countries, and how does CBP share 
biometric information with other countries and what steps does 
it take to ensure that those countries use the data 
responsibly?
    Is that accurate, No. 1, what my understanding is?
    How are we guarding that data to make sure that they are 
protected it?
    Mr. Wagner. I am trying to think of when. I am not aware of 
how we would share or if we are even sharing.
    Mr. Langevin. With the Trusted Traveler Program.
    Mr. Wagner. We do not share. We might share a person's 
status that they are approved in the program, but we are not 
actually sharing, say, their fingerprints.
    Mr. Langevin. OK. So let me ask that one for the record, 
and I would ask that you get back to me on that.
    Mr. Wagner. Yes.
    Mr. Langevin. This is important.
    What types of information do we share under the Trusted 
Traveler Program? I think that is important for us to know.
    If we do share, whatever information we share, I want to 
know what steps we take to ensure that those countries use that 
data responsibly.
    So I know that this question has been touched on earlier. 
So I am going to ask it perhaps in a different way, but just 
prior to our hearing on this topic, last July we were notified 
of a cyber incident on the network of a CBP subcontractor. 
Someone claiming to be a foreign agent gained access to tens of 
thousands of photos of driver's faces and license plates at a 
port of entry along the Southern Border.
    How is CBP ensuring that the personal data it collects for 
facial recognition technology screening programs, whether by 
the Government directly or by vendors or their private-sector 
partners, are being protected from inadvertent or otherwise 
unauthorized access?
    Also, what assurances can you give our committee that the 
root causes of the May 2019 breach have been addressed so as to 
reduce the likelihood of another breach?
    Mr. Wagner. So the airlines and airports that provide the 
cameras that take the pictures to transmit them to CBP, we have 
a signed set of business requirements with them which they 
commit to not storing, not sharing, not saving any of the 
photographs that they take.
    They take the picture, have to transmit it to us, and purge 
it from their system.
    One of the other conditions is that they have to be 
available for CBP to audit their cameras and their technology 
to ensure that they are following those rules.
    We are about to commence an audit on one of the airlines in 
the next couple of months and start that process to do that, 
but to make sure that that is not happening.
    Mr. Langevin. Thank you.
    Mr. Chairman, I would ask that Mr. Wagner get back with me 
in writing as soon as possible on that Trusted Traveler Program 
and what information is shared with partners.
    Chairman Thompson. OK.
    Mr. Langevin. Thank you, Mr. Chairman.
    Chairman Thompson. Thank you.
    Did the gentlelady from Texas want to ask a question since 
everybody else has asked theirs?
    Ms. Jackson Lee. Yes, Mr. Chairman. Thank you so very much.
    First of all, I will ask unanimous consent to place in the 
record, not to the witnesses, but the headline reads, ``Amazon 
Facial Recognition Mistakenly Confused 28 Congressmen with 
Known Criminals,'' July 26, 2018.
    Chairman Thompson. Without objection.
    [The information follows:]
           Article Submitted by Honorable Sheila Jackson Lee
Amazon facial recognition mistakenly confused 28 Congressmen with known 
                               criminals
By Sean Hollister, July 26, 2018, 12:45 PM PDT
https://www.cnet.com/news/amazon-facial-recognition-thinks-28-
        congressmen-look-like-known-criminals-at-default-settings/
    The ACLU says it's evidence that Congress should step in. Amazon 
says the ACLU didn't test properly.
    Amazon is trying to sell its Rekognition facial recognition 
technology to law enforcement, but the American Civil Liberties Union 
doesn't think that's a very good idea. And today, the ACLU provided 
some seemingly compelling evidence--by using Amazon's own tool to 
compare 25,000 criminal mugshots to Members of Congress.
    Sure enough, Amazon's tool thought 28 different Members of Congress 
looked like people who've been arrested.
    Here's the full list, according to the ACLU:
    Senate:
    Johnny Isakson (R-Georgia)
    Ed Markey (D-Massachusetts)
    Pat Roberts (R-Kansas)
    House:
    Sanford Bishop (D-Georgia)
    G.K. Butterfield (D-North Carolina)
    Lacy Clay (D-Missouri)
    Mark DeSaulnier (D-California)
    Adriano Espaillat (D-New York)
    Ruben Gallego (D-Arizona)
    Tom Garrett (R-Virginia)
    Greg Gianforte (R-Montana)
    Jimmy Gomez (D-California)
    Raul Grijalva (D-Arizona)
    Luis Gutierrez (D-Illinois)
    Steve Knight (R-California)
    Leonard Lance (R-New Jersey)
    John Lewis (D-Georgia)
    Frank LoBiondo (R-New Jersey)
    Dave Loebsack (D-Iowa)
    David McKinley (R-West Virginia)
    John Moolenaar (R-Michigan)
    Tom Reed (R-New York)
    Bobby Rush (D-Illinois)
    Norma Torres (D-California)
    Marc Veasey (D-Texas)
    Brad Wenstrup (R-Ohio)
    Steve Womack (R-Arkansas)
    Lee Zeldin (R-New York)
    That's a lot of Congresspeople who may soon have some very valid 
questions about facial recognition and its potential to be abused--
particularly since Amazon thinks the ACLU didn't use it properly to 
begin with! It turns out that the ACLU got its mugshot matches by using 
the Rekognition software at its default 80-percent confidence threshold 
setting, rather than the 95-percent-plus confidence level that Amazon 
recommends for law enforcement agencies.
    It turns out that the ACLU got its mugshot matches by using the 
Rekognition software at its default 80-percent confidence threshold 
setting, rather than the 95-percent plus confidence level that Amazon 
recommends for law enforcement agencies.
    ``While 80 percent confidence is an acceptable threshold for photos 
of hot dogs, chairs, animals, or other social media use cases, it 
wouldn't be appropriate for identifying individuals with a reasonable 
level of certainty. When using facial recognition for law enforcement 
activities, we guide customers to set a threshold of at least 95 
percent or higher,'' an Amazon spokesperson told CNET by email.
    But an ACLU lawyer tells CNET that Amazon doesn't necessarily steer 
law enforcement agencies toward that higher threshold--if a police 
department uses the software, it'll be set to the same 80-percent 
threshold by default and won't ask them to change it even if they 
intend to use it to identify criminals. ``Amazon makes no effort to ask 
users what they are using Rekognition for,'' says ACLU attorney Jacob 
Snow.
    A source close to the matter tells CNET that when Amazon works with 
law enforcement agencies directly, like the Orlando Police Department, 
it teaches them how to reduce false positives and avoid human bias. But 
there's nothing to necessarily keep other agencies from simply using 
the tool the same way the ACLU did, instead of developing a 
relationship with Amazon.
    It's worth noting that false positives are (currently!) an accepted 
part of facial recognition technology. Nobody--including the ACLU--is 
saying police would arrest someone based on a false match alone. Facial 
recognition narrows down the list of suspects, and then humans take 
over. Recently, facial recognition helped ID the Russian assassins who 
poisoned a spy in the UK, as well as the Capital Gazette shooter.
    And Amazon didn't actually create that many false positives even at 
the 80 percent confidence ratio, compared to, say, the UK Metropolitan 
Police's facial recognition tech.
    But the ACLU worries that Amazon's false positives might bias a 
police officer or government agent to search, question, or potentially 
draw a weapon when they shouldn't--and we've all seen how those 
encounters can turn deadly. And the ACLU notes that Amazon's tech seems 
to have over-represented people of color.
    Should Congress regulate facial recognition? Microsoft thinks so, 
and now 28 Members of Congress have some very personal food for 
thought--95-percent confidence threshold or no.
    In the hours since the ACLU's test was brought to light, five 
Congressmen have sent letters to Amazon CEO Jeff Bezos asking for 
answers and an immediate meeting. You can read the letters here.

    Ms. Jackson Lee. So, Mr. Wagner, I just wanted to ask you. 
Are you using Amazon technology?
    Mr. Wagner. We are not using their matching algorithm.
    Ms. Jackson Lee. Thank you, Mr. Chairman.
    My question is you gave Congressman Langevin sort-of a 
detailed response. So let me try to change it around to: Do you 
have a team that is directly responsible not just for the 
implementation, but for the internal analysis of the 
utilization of the app or the technology that you are using so 
that it is on-site, so you are able to get first-hand knowledge 
of the violations or let me use the word ``abuses'' by way of 
the technology?
    Is that information coming back to your office? When I say 
that, to your sector.
    Mr. Wagner. Yes. Part of it is our office that does it, and 
then working in conjunction with our field locations.
    Ms. Jackson Lee. So do you have a team that is just 
responding to that, if you would?
    Mr. Wagner. We have teams that review the data, review the 
reports, review the functioning of the systems, review the 
compliance of the officers using the technology, yes.
    Ms. Jackson Lee. Mr. Chairman, I will just say this. I know 
we have a lot of work. You have a lot of work. You do a lot, 
but maybe there could be a Classified briefing.
    I would like to do a deeper dive on how that is done and 
how that is kept and whether they store, how long they keep the 
data on Mr. Jones or Mr. Aman and Mr. various named persons, 
how long they keep the data.
    Chairman Thompson. Well, we will work through it.
    Mr. Wagner. The data is all stored in compliance with the 
Systems of Record Notices of where that data is stored. So the 
photograph of the U.S. citizen that we take is only stored for 
12 hours and then purged.
    A picture of a foreign national is sent over to IDENT, the 
Department's database, where it is stored for 75 years.
    The record of the border crossing, the biographical 
information is then stored in other systems attributable to the 
System of Record Notices attributable to those.
    Ms. Jackson Lee. Very interesting. Thank you.
    Chairman Thompson. We will follow up on your request.
    Ms. Jackson Lee. Thank you, witnesses. Thank you.
    Chairman Thompson. Let me insert in the record a letter 
from the Electronic Privacy Information Center and a press 
release from the U.S. Travel Association.
    [The information follows:]
         Letter From the Electronic Privacy Information Center
                                  February 5, 2020.
The Honorable Bennie G. Thompson, Chairman,
The Honorable Mike Rogers, Ranking Member,
Committee on Homeland Security, U.S. House of Representatives, H2-176 
        Ford House Office Building, Washington, DC 20515.
    Dear Chairman Thompson and Ranking Member Rogers: We write to you 
in advance of the hearing on ``About Face: Examining the Department of 
Homeland Security's Use of Facial Recognition and Other Biometric 
Technologies, Part II.''\1\ EPIC supports a moratorium on facial 
recognition technology for mass surveillance. This committee should 
halt DHS's use of face surveillance technology.
---------------------------------------------------------------------------
    \1\ About Face: Examining the Department of Homeland Security's Use 
of Facial Recognition and Other Biometric Technologies, Part II, House 
Comm. on Homeland Security, 116th Cong. (Feb. 6, 2020), https://
homeland.house.gov/activities/hearings/about-face-examining-the-
department-of-homeland-securitys-use-of-facial-recognition-and-other-
biometric-technologies-part-ii.
---------------------------------------------------------------------------
    The Electronic Privacy Information Center (``EPIC'') is a public 
interest research center established in 1994 to focus public attention 
on emerging privacy and civil liberties issues.\2\ EPIC is focused on 
protecting individual privacy rights, and we are particularly 
interested in the privacy problems associated with surveillance.\3\ 
Last year, EPIC filed a lawsuit against the Customs and Border 
Protection (``CBP'') agency for failure to establish necessary privacy 
safeguards for the collection of facial images at U.S. borders.\4\
---------------------------------------------------------------------------
    \2\ See About EPIC, EPIC.org, https://epic.org/epic/about.html.
    \3\ EPIC, EPIC Domestic Surveillance Project, https://epic.org/
privacy/surveillance/.
    \4\ EPIC v. U.S. Customs and Border Protection, No. 19-cv-689 
(D.D.C. filed Mar. 12, 2019); See https://epic.org/foia/dhs/cbp/alt-
screening-procedures/.
---------------------------------------------------------------------------
                    a call to ban face surveillance
    EPIC and the Public Voice Coalition are leading a global campaign 
to establish a moratorium on ``face surveillance,'' the use of facial 
recognition for mass surveillance.\5\ In October 2019 more than 100 
NGO's and hundreds of experts endorsed our petition.\6\ The signatories 
stated:
---------------------------------------------------------------------------
    \5\ EPIC, Ban Face Surveillance, https://epic.org/
banfacesurveillance/.
    \6\ The Public Voice, Declaration: A Moratorium on Facial 
Recognition Technology for Mass Surveillance Endorsements, https://
thepublicvoice.org/ban-facial-recognition/endorsement/.
---------------------------------------------------------------------------
   We urge countries to suspend the further deployment of 
        facial recognition technology for mass surveillance;
   We urge countries to review all facial recognition systems 
        to determine whether personal data was obtained lawfully and to 
        destroy data that was obtained unlawfully;
   We urge countries to undertake research to assess bias, 
        privacy and data protection, risk, and cyber vulnerability, as 
        well as the ethical, legal, and social implications associated 
        with the deployment of facial recognition technologies; and
   We urge countries to establish the legal rules, technical 
        standards, and ethical guidelines necessary to safeguard 
        fundamental rights and comply with legal obligations before 
        further deployment of this technology occurs.
    Courts and regulators are also listening. There is growing 
awareness of the need to bring this technology to a halt. The State of 
California prohibited the use facial recognition on police-worn body 
cameras. Several cities in the U.S. have banned the use of facial 
recognition systems, and there is a growing protest around the world. 
For example, In 2019 the Swedish Data Protection Authority prohibited 
the use of facial recognition in schools. EPIC has published a resource 
of laws, regulations, legal decisions and reports on face surveillance 
worldwide at https://epic.org/banfacesurveillance/.
                 threats to privacy and civil liberties
    Facial recognition poses serious threats to privacy and civil 
liberties and can be deployed covertly, remotely, and on a mass scale. 
There is a lack of well-defined regulations controlling the collection, 
use, dissemination, and retention of biometric identifiers. Ubiquitous 
identification by commercial or Government entities eliminates the 
individual's ability to control the disclosure of their identities, 
creates new opportunities for tracking and monitoring, increases the 
security risks from data breaches. An individual's ability to control 
disclosure of his or her identity is an essential aspect of personal 
freedom and autonomy. The use of facial recognition erodes these 
freedoms.
    There is little a person in the United States could do to prevent 
the capture of their image by the Government or a private company if 
face surveillance is deployed. Participation in society necessarily 
requires participation in public spaces. But ubiquitous and near 
effortless identification eliminates the individual's ability to 
control the disclosure of their identities to others. Strangers will 
know our identities as readily as our friends and family members.
                   use of face surveillance in china
    Face surveillance capabilities have been on full display in China. 
China is not only the leading government for face surveillance 
technology, it is also the leading exporter of the technology.\7\ The 
Chinese government has implemented a massive facial recognition 
surveillance system.\8\ China has leveraged its surveillance network to 
implement an ``advanced facial recognition technology to track and 
control the Uighurs, a largely Muslim minority.''\9\ And China 
continues to expand the use of facial recognition technology. A 
university in China is testing the use of facial recognition to monitor 
whether students attend classes and to track their attention during 
lectures.\10\ To register a new mobile phone number in China now 
requires one to submit to a facial scan.\11\ Trials have also begun to 
use facial recognition at security checkpoints in the subway 
system.\12\
---------------------------------------------------------------------------
    \7\ Steven Feldstein, The Global Expansion of AI Surveillance 13-15 
(Sept. 2019), https://carnegieendowment.org/files/WP-Feldstein-
AISurveillance_final1.pdf.
    \8\ Simon Denyer, China's Watchful Eye, Wash. Post (Jan. 7, 2018), 
https://www.washingtonpost.com/news/world/wp/2018/01/07/feature/in-
china-facial-recognition-is-sharp-end-of-a-drive-for-total-
surveillance/.
    \9\ Paul Mozur, One Month, 500,000 Face Scans: How China is Using 
A.I. to Profile a Minority, N.Y. Times (Apr. 14, 2019), https://
www.nytimes.com/2019/04/14/technology/china-surveillance-artificial-
intelligence-racial-profiling.html.
    \10\ Brendan Cole, Chinese University Tests Facial Recognition 
System to Monitor Attendance and Students' Attention to Lectures, 
Newsweek (Sept. 2, 2019), https://www.newsweek.com/nanjing-china-
facial-recognition-1457193.
    \11\ Kyle Wiggers, AI Weekly: In China, You Can No Longer Buy a 
Smartphone without a Face Scan, VentureBeat (Oct. 11, 2019), https://
venturebeat.com/2019/10/11/ai-weekly-in-china-you-can-no-longer-buy-a-
smartphone-without-a-face-scan/.
    \12\ Wan Lin, Beijing Subway Station Trials Facial Recognition, 
Global Times (Dec. 1, 2019), http://www.globaltimes.cn/content/
1171888.shtml.
---------------------------------------------------------------------------
    In Hong Kong, where protests have been on-going since March, face 
scans have become a weapon. Protesters fear that facial recognition 
technology is being used to identify and track them.\13\ In response to 
this fear, protesters have resorted to covering their faces and have 
taken down facial recognition cameras. Hong Kong reacted by banning 
masks and face paint.\14\ Many of the demonstrators worry that the mass 
surveillance implemented on the mainland of China will be implemented 
in Hong Kong.
---------------------------------------------------------------------------
    \13\ Paul Mozur, In Hong Kong Protests, Faces Become Weapons, N.Y. 
Times (July 26, 2019), https://www.nytimes.com/2019/07/26/technology/
hong-kong-protests-facial-recognition-surveillance.html.
    \14\ Matt Novak, Hong Kong Announces Ban on Masks and Face Paint 
That Helps Protesters Evade Facial Recognition, Gizmodo (Oct. 4, 2019), 
https://gizmodo.com/hong-kong-announces-ban-on-masks-and-face-paint-
that-he-1838765030.
---------------------------------------------------------------------------
                 face surveillance in the united states
    The implementation of facial recognition technology by Government 
and commercial actors in the United States is pushing the U.S. toward a 
similar mass surveillance infrastructure. Already some schools are 
implementing the use of facial recognition technology.\15\ Customs and 
Border Protection (CBP) is using facial recognition on travelers 
entering and exiting the U.S.\16\ And airlines are using CBP's facial 
recognition system to conduct flight check-ins, check bags, and board 
flights.\17\ The Rochester airport has implemented the surveillance 
infrastructure to perform facial recognition on every person that 
enters the airport.\18\ Amazon drafted plans to use their Ring 
surveillance cameras to create neighborhood watch lists that leverage 
facial recognition.\19\ Retailers have implemented the use of facial 
recognition at their stores.\20\ A landlord in Brooklyn wanted to use 
facial recognition as the means to gain entry into a rent-stabilized 
apartment building.\21\ Facial recognition is being used at major 
sporting events \22\ and concerts.\23\ And the companies that are 
creating the facial recognition algorithms are often using--without 
consent--millions of photos scraped from social media sites and other 
webpages in order train the algorithms.\24\
---------------------------------------------------------------------------
    \15\ Tom Simonite and Gregory Barber, The Delicate Ethics of Using 
Facial Recognition in Schools, Wired (Oct. 17, 2019), https://
www.wired.com/story/delicate-ethics-facial-recognition-schools/.
    \16\ Davey Alba, The US Government Will Be Scanning Your Face At 20 
Top Airports, Documents Show, BuzzFeed (Mar. 11, 2019), https://
www.buzzfeednews.com/article/daveyalba/these-documents-reveal-the-
governments-detailed-plan-for?ref=bfnsplash.
    \17\ See, e.g., Kathryn Steele, Delta Unveils First Biometric 
Terminal in U.S. in Atlanta; next stop: Detroit, Delta News Hub, 
https://news.delta.com/delta-unveils-first-biometric-terminal-us-
atlanta-next-stop-detroit.
    \18\ James Gilbert, Facial Recognition Heading to Rochester Airport 
Despite Concerns, Rochester First (June 26, 2019), https://
www.rochesterfirst.com/news/local-news/facial-recognition-heading-to-
airport-despite-concerns/.
    \19\ Sam Biddle, Amazon's Ring Planned Neighborhood ``Watch Lists 
`` Built on Facial Recognition, The Intercept (Nov. 26, 2019), https://
theintercept.com/2019/11/26/amazon-ring-home-security-facial-
recognition/.
    \20\ Nick Tabor, Smile! The Secretive Business of Facial-
Recognition Software in Retails Stores, New York Intelligencer (Oct. 
20, 2018), http://nymag.com/intelligencer/2018/10/retailers-are-using-
facial-recognition-technology-too.html.
    \21\ Gina Bellafante, The Landlord Wants Facial Recognition in Its 
Rent-Stabilized Buildings. Why?, N.Y. Times (Mar. 28, 2019), https://
www.nytimes.com/2019/03/28/nyregion/rent-stabilized-buildings-facial-
recognition.html.
    \22\ Ryan Rodenberg, Sports Betting and Big Brother: Rise of Facial 
Recognition Cameras, ESPN (Oct. 3, 2018 ), https://www.espn.com/chalk/
story/_/id/24884024/why-use-facial-recognition-cameras-sporting-events-
the-rise.
    \23\ Steve Knopper, Why Taylor Swift Is Using Facial Recognition at 
Concerts, Rolling Stone (Dec. 13, 2018), https://www.rollingstone.corn/
music/music-news/taylor-swift-facial-recognition-concerts-768741/.
    \24\ Kashmir Hill, The Secretive Company That Might End Privacy as 
We Know It, N.Y. Times (Jan. 18, 2020), https://www.nytimes.com/2020/
01/18/technology/clearview-privacy-facial-recognition.html.
---------------------------------------------------------------------------
    It is important to note that not all uses of facial recognition are 
equally problematic. For instance, where the user has control and there 
is no Government mandate, such as using Face ID for iPhone 
authentication, the same privacy issues do not arise. Facial 
recognition can also be used for verification or authentication using 
1:1 matching--that is, where the system does not check every record in 
a database for a match, but matches the individual's face to their 
claimed identity.\25\ This 1:1 matching is a much more privacy 
protective implementation of facial recognition. 1:1 matching does not 
require a massive biometric database, there is no need to retain the 
image, and the machines conducting the 1:1 match do not need to be 
connected to the cloud. Such an implementation virtually eliminates 
data breach risks and the chance of mission creep.
---------------------------------------------------------------------------
    \25\ Lucas D. lntrona and Helen Nissenbaum, Facial Recognition 
Technology: A Survey of Policy and Implementation Issues, Ctr. for 
Catastrophe Preparedness & Response, N.Y. Univ., 11 (2009), available 
at https://nissenbaum.tech.cornell.edu/papers/
facial_recognition_report.pdf.
---------------------------------------------------------------------------
                     face surveillance in airports
    Recently, new privacy risks have arisen with the deployment of 
facial recognition technology at U.S. airports following a 2017 
Executive Order to ``expedite the completion and implementation of 
biometric entry exit tracking system.''\26\ Customs and Border 
Protection (``CBP'') has now implemented the Biometric Entry-Exit 
program for international travelers at 17 airports.\27\ TSA is quickly 
moving to leverage CBP's Biometric Entry-Exit program to expand the use 
of facial recognition at airports.\28\
---------------------------------------------------------------------------
    \26\ Exec. Order No. 13,780  8.
    \27\ Davey Alba, The US Government Will Be Scanning Your Face At 20 
Top Airports, Documents Show (Mar. 11, 2019), https://
www.buzzfeednews.corn/article/daveyalba/these-documents-reveal-the-
governments-detailed-plan-for.
    \28\ TSA, TSA Biometrics Roadmap (Sept. 2018), https://www.tsa.gov/
sites/default/files/tsa_biometrics_roadmap.pdf.
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    TSA has already deployed facial recognition technology at two TSA 
Checkpoints.\29\ In September 2018, TSA released a ``TSA Biometrics 
Roadmap,'' detailing its plans to use facial recognition, including on 
domestic travelers.\30\ The Roadmap makes clears TSA's intention to 
leverage CBP's facial recognition capabilities implemented as part of 
the Biometric Entry-Exit Program. But corresponding privacy safeguards 
have not yet been established.
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    \29\ Trans. Security Admin., Travel Document Checker Automation 
Using Facial Recognition, (Aug. 2019), https://www.dhs.gov/publication/
dhstsapia-046-travel-document-checker-automation-using-facial-
recognition; U.S. Customs and Border Protection, CBP Deploys Facial 
Recognition Biometric Technology at 1 TSA Checkpoint at JFK Airport 
(Oct. 11, 2017), https://www.cbp.gov/newsroom/national-media-release/
cbp-deploys-facial-recognition-biometric-techno- logy-1-tsa-checkpoint.
    \30\ TSA, TSA Biometrics Roadmap (Sept. 2018), https ://
www.tsa.gov/sites/default/files/tsa_biometrics_roadmap.pdf.
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    In response to EPIC's Freedom of Information Act request, CBP 
recently released 346 pages of documents detailing the agency's 
scramble to implement the flawed Biometric Entry-Exit system, a system 
that employs facial recognition technology on travelers entering and 
exiting the country. The documents obtained by EPIC describe the 
administration's plan to extend the faulty pilot program to major U.S. 
airports. The documents obtained by EPIC were covered in-depth by 
Buzzfeed.\31\
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    \31\ Davey Alba, The US Government Will Be Scanning Your Face At 20 
Top Airports, Documents Show (Mar. 11, 2019), https://
www.buzzfeednews.com/article/daveyalba/these-documents-reveal-the-
govemments-detailed-plan-for.
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    Based on the documents obtained, EPIC determined that there are few 
limits on how airlines will use the facial recognition data collected 
at airports.\32\ Only recently has CBP changed course and indicated 
that the agency will require airlines to delete the photos they take 
for the Biometric Entry-Exit program.\33\ No such commitment has been 
made by TSA. Indeed, TSA's Roadmap indicates that the agency wants to 
expand the dissemination of biometric data as much as possible, 
stating:
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    \32\ See CBP Memorandum of Understanding Regarding Biometric Pilot 
Project, https://epic.org/foia/dhs/cbp/biometric-entry-exit/MOU-
Biometric-Pilot-Project.pdf.
    \33\ Ashley Ortiz, CBP Program and Management Analyst, Presentation 
before the Data Privacy & Integrity Advisory Committee, slide 23 (Dec. 
2018), https://www.dhs.gov/sites/default/files/publications/SLIDES-
DPIAC-Public%20Meeting%2012%2010-2018.pdf.

``TSA will pursue a system architecture that promotes data sharing to 
maximize biometric adoption throughout the passenger base and across 
the aviation security touch points of the passenger experience.''\34\
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    \34\ TSA, TSA Biometrics Roadmap, 17 (Sept. 2018).

    TSA seeks to broadly implement facial recognition through ``public-
private partnerships'' in an effort to create a ``biometrically-enabled 
curb-to-gate passenger experience.''\35\ Currently, TSA plans to 
implement an opt-in model of facial recognition use for domestic 
travelers but there are no guarantees that in the future TSA will not 
require passengers to participate in facial recognition or make the 
alternative so cumbersome as to essentially require passengers to opt-
in.
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    \35\ Id. at 19.
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    Preserving the ability of U.S. citizens to forgo facial recognition 
for alternative processes is one of the privacy issues with CBP's 
Biometric Entry-Exit program. Senator Markey (D-MA) and Senator Lee (R-
UT) called for the CBP to suspend facial recognition at the border to 
ensure that travelers are able to opt out of facial recognition if they 
wish.\36\
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    \36\ Press Release, Sens. Edward Markey and Mike Lee, Senators 
Markey and Lee Call for Transparency on DHS Use of Facial Recognition 
Technology (Mar. 12, 2019), https://www.markey.senate.gov/news/press-
releases/senators-markey-and-lee-call-for-transparency-on-dhs-use-of-
facial-recognition-technology.
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    In fact, EPIC recently sued CBP for all records related to the 
creation and modification of alternative screening procedures for the 
Biometric Entry-Exit program.\37\ The alternative screening procedure 
for U.S. travelers that opt out of facial recognition should be a 
manual check of the traveler's identification documents. CBP, however, 
has provided vague and inconsistent descriptions of alternative 
screening procedures in both its ``Biometric Exit Frequently Asked 
Questions (FAQ)'' webpage \38\ and the agency's privacy impact 
assessments.\39\ The creation and modification of CBP's alternative 
screening procedures underscores CBP's unchecked ability to modify 
alternative screening procedures while travelers remain in the dark 
about how to protect their biometric data.
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    \37\ EPIC v. CBP, 19-cv-00689, Complaint, https://epic.org/foia/
cbp/altemative-screening-procedures/1-Complaint.pdf.
    \38\ CBP, Biometric Exit Frequently Asked Questions (FAQs), https:/
/www.cbp.gov/travel/biometrics/biometric-exit-faqs.
    \39\ U.S. Dep't of Homeland Sec., DHS/CBP/PIA-030(b), Privacy 
Impact Assessment Update for the Traveler Verification Service (TVS): 
Partner Process 8 (2017), https://www.dhs.gov/sites/default/files/
publications/privacy-pia-cbp030-tvs-may2017.pdf; see also U.S. Dep't of 
Homeland Sec., DHS/CBP/PIA-030(c), Privacy Impact Assessment Update for 
the Traveler Verification Service (TVS): Partner Process 5-6 (2017), 
https://www.dhs.gov/sites/default/files/publications/privacy-pia-
cbp030-tvs-appendixb-july2018.pdf; U.S. Dep't of Homeland Sec., DHS/
CBP/PIA-056, Privacy Impact Assessment for the Traveler Verification 
Service 2 (2018), https://www.dhs.gov/sites/default/files/publications/
privacy-pia-cbp-0-tvs-november2018_2.pdf.
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                        face surveillance and ai
    It is also becoming increasingly clear that AI tools are being 
deployed with facial recognition to accelerate the deployment of 
technology not only for identification but also for scoring. As we 
explained recently in the New York Times, ``The United States must work 
with other democratic countries to establish red lines for certain AI 
applications and ensure fairness, accountability, and transparency as 
AI systems are deployed.''\40\ In a subsequent letter to the New York 
Times, we warned of the growing risk of the Chinese AI model, and 
specifically explained, ``China also dominates the standards-setting 
process for techniques like facial recognition.''\41\
---------------------------------------------------------------------------
    \40\ Marc Rotenberg, The Battle Over Artificial Intelligence, N.Y. 
Times, Apr. 18, 2019, https://www.nytimes.com/2019/04/18/opinion/
letters/artificial-intelligence.html. In the introduction to the EPIC 
AI Policy Sourceboook and in a subsequent letter to the New York Times, 
we warned of the growing risk of the Chinese AI model. Marc Rotenberg 
and Len Kennedy, Surveillance in China: Implications for Americans, 
N.Y. Times, Dec. 19, 2019, https://www.nytimes.com/2019/12/19/opinion/
letters/surveillance-china.html.
    \41\ Marc Rotenberg and Len Kennedy, Surveillance in China: 
Implications for Americans, N.Y. Times, Dec. 19, 2019, https://
www.nytimes.com/2019/12/19/opinion/letters/surveillance-china.html.
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    Society is simply not in a place right now for the wide-scale 
deployment of facial recognition technology. It would be a mistake to 
deploy facial recognition at this time. We urge the committee to 
support a ban of DHS's further deployment of face surveillance 
technology.
    We ask that this statement be entered in the hearing record. EPIC 
looks forward to working with the committee on these issues of vital 
importance to the American public.
            Sincerely,
                                            Marc Rotenberg,
                                                    EPIC President.
                                      Caitriona Fitzgerald,
                                              EPIC Policy Director.
                                             Jeramie Scott,
                                               EPIC Senior Counsel.
Attachment.--Declaration: A Moratorium on Facial Recognition Technology 
for Mass Surveillance, The Public Voice, Tirana Albania (October 2019).
                                 ______
                                 
Attachment.--Declaration: A Moratorium on Facial Recognition Technology 
                         for Mass Surveillance
October 2019, Tirana, Albania
    We the undersigned call for a moratorium on the use of facial 
recognition technology that enables mass surveillance.
    We recognize the increasing use of this technology for commercial 
services, Government administration, and policing functions. But the 
technology has evolved from a collection of niche systems to a powerful 
integrated network capable of mass surveillance and political control.
    Facial recognition is now deployed for human identification, 
behavioral assessment, and predictive analysis.
    Unlike other forms of biometric technology, facial recognition is 
capable of scrutinizing entire urban areas, capturing the identities of 
tens or hundreds of thousands of people at any one time.
    Facial recognition can amplify identification asymmetry as it tends 
to be invisible or at best, opaque.
    Facial recognition can be deployed in almost every dimension of 
life, from banking and commerce to transportation and communications.
    We acknowledge that some facial recognition techniques enable 
authentication for the benefit of the user. However facial recognition 
also enables the development of semi-autonomous processes that minimize 
the roles of humans in decision making.
    We note with alarm recent reports about bias, coercion, and fraud 
in the collection of facial images and the use of facial recognition 
techniques. Images are collected and used with forced consent or 
without consent at all.
    We recall that in the 2009 Madrid Declaration, civil society called 
for a moratorium on the development or implementation of facial 
recognition, subject to a full and transparent evaluation by 
independent authorities and through democratic debate.
    There is growing awareness of the need for a moratorium. In 2019 
the Swedish Data Protection Authority prohibited the use of facial 
recognition in schools. The State of California prohibited the use 
facial recognition on police-worn body cameras. Several cities in the 
United States have banned the use of facial recognition systems, and 
there is growing protest around the world.
    Therefore:
    1. We urge countries to suspend the further deployment of facial 
        recognition technology for mass surveillance;
    2. We urge countries to review all facial recognition systems to 
        determine whether personal data was obtained lawfully and to 
        destroy data that was obtained unlawfully;
    3. We urge countries to undertake research to assess bias, privacy 
        and data protection, risk, and cyber vulnerability, as well as 
        the ethical, legal, and social implications associated with the 
        deployment of facial recognition technologies; and
    4. We urge countries to establish the legal rules, technical 
        standards, and ethical guidelines necessary to safeguard 
        fundamental rights and comply with legal obligations before 
        further deployment of this technology occurs.

https://thepublicvoice.org/ban-facial-recognition/
                                 ______
                                 
                 News Release, U.S. Travel Association
U.S. Travel Reacts to Suspension of Global Entry for New York Residents
    WASHINGTON (February 6, 2020).--U.S. Travel Association Executive 
Vice President for Public Affairs and Policy Tori Emerson Barnes issued 
the following statement on the reported suspension of Global Entry and 
several other trusted traveler programs for residents of the State of 
New York:

``Travel should not be politicized. Trusted traveler programs enhance 
our national security because they provide greater certainty regarding 
a person's identity, citizenship, and criminal background. Suspending 
enrollment in Global Entry and other trusted traveler programs only 
undermines travel security and efficiency. We are in contact with the 
Department of Homeland Security to convey this message.''
Contacts
    Chris Kennedy: (O) 202.218.3603 (C) 202.465.6635
    Tim Alford: (O) 202.218.3625 (C) 740.215.1290
###
U.S. Travel Association is the national, non-profit organization 
representing all components of the travel industry that generates $2.5 
trillion in economic output and supports 15.7 million jobs. U.S. 
Travel's mission is to increase travel to and within the United States. 
Visit www.ustravel.org.

    Chairman Thompson. Mr. Wagner, if you are aware of any 
notification requirements that a State would be noticed, I am 
talking about the global entry situation because it looks like 
New York is just the first of 1 or 2 others.
    Since we have been sitting here, Mr. Cuccinelli has said 
Washington State might be in a similar position.
    I am just wanting to make sure that if this is the way 
forward, then surely, in light of what Mr. Rose and some of the 
other New Yorkers on this committee have said, there should be 
some notice that this is about to happen and not just a press 
conference.
    So if you are aware of any, please get it back to us in the 
committee. We would love to have it.
    I thank the witnesses for their valuable testimony and the 
Members for their questions.
    The Members of the committee may have additional questions 
for the witnesses, and we ask that you respond expeditiously, 
in writing, to those questions.
    Without objection, the committee record will be kept open 
for 10 days.
    Hearing no further business, the committee stands 
adjourned.
    [Whereupon, at 12:15 p.m., the committee was adjourned.]

                                 [all]