Face Recognition: Too Bias, or Not Too Bias?

Understand

We reveal critical insights into problems of bias in state-of-the-art facial recognition (FR) systems using a novel Balanced Faces In the Wild (BFW) dataset: data balanced for gender and ethnic groups.

• We show variations in the optimal scoring threshold for face-pairs across different subgroups.
• Thus, the conventional approach of learning a global threshold for all pairs resulting in performance gaps among subgroups.
• By learning subgroup-specific thresholds, we not only mitigate problems in performance gaps but also show a notable boost in the overall performance.