Face Recognition: Too Bias, or Not Too Bias?

Joseph P. Robinson, Gennady Livitz, Yann Henon, Can Qin, Yun Fu, Samson Timoner; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 0-1

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 results in performance gaps between subgroups. By learning subgroup-specific thresholds, we reduce performance gaps, and also show a notable boost in overall performance. Furthermore, we do a human evaluation to measure bias in humans, which supports the hypothesis that an analogous bias exists in human perception. For the BFW database, source code, and more, visit https://github.com/visionjo/facerec-bias-bfw.

Related Material


[pdf]
[bibtex]
@InProceedings{Robinson_2020_CVPR_Workshops,
author = {Robinson, Joseph P. and Livitz, Gennady and Henon, Yann and Qin, Can and Fu, Yun and Timoner, Samson},
title = {Face Recognition: Too Bias, or Not Too Bias?},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2020}
}