United States Government Publishing Office publisher pbl distributor dst United States Commerce Department National Institute of Standards and Technology (NIST) author aut Government Organization Ngan, Mei. author aut text government publication eng GOVPUB Executive Agency Publications executive 2023-10-27 Commerce Department 2022-07-29 monographic deposited born digital 36 digital object pages C 13. https://www.govinfo.gov/app/details/GOVPUB-C13-cb3ceb9abf4887bc2667acaa810140a4 P0b002ee1c1bb2b7d on1389890416 (OCoLC)1389890416 DGPO 2023-10-27 2026-06-27 GOVPUB-C13-cb3ceb9abf4887bc2667acaa810140a4 machine generated eng fdlp C13 GOVPUB-C13-cb3ceb9abf4887bc2667acaa810140a4 cb3ceb9abf4887bc2667acaa810140a4 GOVPUB NIST Interagency or Internal Reports NOTE: THE “DATE ISSUED” ABOVE MAY DEFAULT TO JANUARY 1ST OF A GIVEN YEAR. TO THE VIEW THE MOST ACCURATE DATE OF ISSUE, REVIEW THE TITLE PAGE OF THE PUBLICATION. Interim or final reports on work performed by NIST for outside sponsors (both government and non-government). May also report results of NIST projects of transitory or limited interest, including those that will be published subsequently in more comprehensive form. National Institute of Standards and Technology Commerce Department. National Institute of Standards and Technology. 2022 2022-07-29 https://www.govinfo.gov/app/details/GOVPUB-C13-cb3ceb9abf4887bc2667acaa810140a4 https://www.govinfo.gov/content/pkg/GOVPUB-C13-cb3ceb9abf4887bc2667acaa810140a4/pdf/GOVPUB-C13-cb3ceb9abf4887bc2667acaa810140a4.pdf Morph - Utility of 1:N Face Recognition for Morph Detection Biometric identification Face morph Human face recognition (Computer science) Morphing (Computer animation) Presentation attack Ngan, Mei. creator Ngan, Mei. Grother, Patrick. Hanaoka, Kayee. National Institute of Standards and Technology (U.S.) Material Measurement Laboratory mdu U.S. Dept. of Commerce, National Institute of Standards and Technology 2022-07-29. monographic 1 online resource (36 pages) : illustrations (color) text technical report eng This report is a part of a series of studies on the topic of face morphing, its relevance and implications as a vulnerability to automated face recognition, and methods to aid in detecting morphs. Expanding on concepts introduced in a study conducted by the Dutch Vehicle Authority, this report presents a methodology and quantitative results on the use of automated one-to-many (1:N) face recognition algorithms as a mechanism to potentially detect the presence of morphs. This report is intended to inform end-users and identity credential issuance entities, especially those that accept user-submitted photos, in understanding how a 1:N search against a centralized database might be used to flag suspicious activity related to face morphing. Our proposed methodology analyzes the rank 1 and rank 2 scores that are returned on candidate lists from searching morph and bona fide photos against both consolidated and unconsolidated galleries of 1.6 million unique subjects under new enrollment and renewal scenarios. Morph classifiers are trained using the rank 1 and 2 score pairs from a number of modern 1:N face recognition algorithms to quantify the utility of these scores in detecting morphs. Mei Ngan; Patrick Grother; Kayee Hanaoka. July 2022. Title from PDF title page (viewed January 4, 2023). Includes bibliographical references. Approved by the NIST Editorial Review Board on 2022-09-20 Mode of access: World Wide Web. Systems requirements: Adobe Acrobat PDF reader. Morphing (Computer animation) Human face recognition (Computer science) Biometric identification Face morph Presentation attack https://doi.org/10.6028/NIST.IR.8430 MORPH - utility of 1:N face recognition for morph detection MORPH - utility of 1:N face recognition for morph detection