4 citations · 5 across the 3 of their papers we have counts for
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cs.CV2023★ 1 cited
Human-Machine Comparison for Cross-Race Face Verification: Race Bias at the Upper Limits of Performance?
Geraldine Jeckeln, Selin Yavuzcan, Kate A. Marquis +4
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 impro…
cs.CV2021★ 4 cited
Distill and De-bias: Mitigating Bias in Face Verification using Knowledge Distillation
Prithviraj Dhar, Joshua Gleason, Aniket Roy +3
Face recognition networks generally demonstrate bias with respect to sensitive attributes like gender, skintone etc. For gender and skintone, we observe that the regions of the fac…