Face Recognition: Too Bias, or Not Too Bias?

• URL: http://arxiv.org/abs/2002.06483v4
• Date: Tue, 21 Apr 2020 01:34:11 GMT
• Title: Face Recognition: Too Bias, or Not Too Bias?
• Authors: Joseph P Robinson, Gennady Livitz, Yann Henon, Can Qin, Yun Fu, and Samson Timoner
• Abstract summary: We reveal critical insights into problems of bias in state-of-the-art facial recognition systems. We show variations in the optimal scoring threshold for face-pairs across different subgroups. We also do a human evaluation to measure the bias in humans, which supports the hypothesis that such bias exists in human perception.
• Score: 45.404162391012726
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Furthermore, we do a human evaluation to measure the bias in humans, which supports the hypothesis that such a bias exists in human perception. For the BFW database, source code, and more, visit github.com/visionjo/facerec-bias-bfw.

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