  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.
