Face Regions Impact Recognition Accuracy Differently Across Demographics
Vítor Albiero, Kevin W. Bowyer, Michael C. King · 2022
Variation in face recognition accuracy across demographic groups has attracted attention from news media, civil liberties advocates and academic researchers. The problem is challenging, in that both the impostor distribution (matches across different people) and the genuine distribution (matches across same people) may vary across demographic groups. Simple answers such as balancing the number of subjects and images in the training data do not have a substantial impact on demographic accuracy disparities. We present the first investigation into whether parts of the face - such as eyes, nose, mouth - show the same accuracy differences across demographic groups as are seen with matching the whole face. We show that matching focused on different parts of the face may result in opposite accuracy differences across demographics. For example, using the eye region for face matching results in Caucasian males having a better impostor distribution (lower similarity scores) than Caucasian females, but using the nose regionfor face matching results in Caucasian females having a better impostor distribution. We also show that it is possible to select face region(s) that effectively minimize the difference in the impostor or genuine distributions across at least some demographics. Our results suggest that a new pathway to reducing accuracy disparity across demographic groups may be to weight the parts of the face differently in matching.