Human-Machine Comparison for Cross-Race Face Verification: Race Bias at the Upper Limits of Performance?

• URL: http://arxiv.org/abs/2305.16443v2
• Date: Wed, 31 May 2023 01:09:57 GMT
• Title: Human-Machine Comparison for Cross-Race Face Verification: Race Bias at the Upper Limits of Performance?
• Authors: Geraldine Jeckeln, Selin Yavuzcan, Kate A. Marquis, Prajay Sandipkumar Mehta, Amy N. Yates, P. Jonathon Phillips, Alice J. O'Toole
• Abstract summary: Face recognition algorithms perform more accurately than humans in some cases, though humans and machines both show race-based accuracy differences. We constructed a challenging test of 'cross-race' face verification and used it to compare humans and two state-of-the-art face recognition systems. We conclude that state-of-the-art systems for identity verification between two frontal face images of Black and White individuals can surpass the general population.
• Score: 0.7036032466145111
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Face recognition algorithms perform more accurately than humans in some cases, though humans and machines both show race-based accuracy differences. As algorithms continue to improve, it is important to continually assess their race bias relative to humans. We constructed a challenging test of 'cross-race' face verification and used it to compare humans and two state-of-the-art face recognition systems. Pairs of same- and different-identity faces of White and Black individuals were selected to be difficult for humans and an open-source implementation of the ArcFace face recognition algorithm from 2019 (5). Human participants (54 Black; 51 White) judged whether face pairs showed the same identity or different identities on a 7-point Likert-type scale. Two top-performing face recognition systems from the Face Recognition Vendor Test-ongoing performed the same test (7). By design, the test proved challenging for humans as a group, who performed above chance, but far less than perfect. Both state-of-the-art face recognition systems scored perfectly (no errors), consequently with equal accuracy for both races. We conclude that state-of-the-art systems for identity verification between two frontal face images of Black and White individuals can surpass the general population. Whether this result generalizes to challenging in-the-wild images is a pressing concern for deploying face recognition systems in unconstrained environments.

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