The Gender Gap in Face Recognition Accuracy Is a Hairy Problem

Aman Bhatta, Vítor Albiero, Kevin W. Bowyer, Michael C. King; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, 2023, pp. 303-312

It is broadly accepted that there is a "gender gap" in|face recognition accuracy, with females having higher false|match and false non-match rates. However, relatively little is known about the cause(s) of this gender gap. We|first demonstrate that female and male hairstyles have important differences that impact face recognition accuracy.|In particular, variation in male facial hair contributes to|a greater average difference in appearance between different male faces. We then demonstrate that when the data|used to evaluate recognition accuracy is gender-balanced|for how hairstyles occlude the face, the initially observed|gender gap in accuracy largely disappears. We show this|result for two different matchers, and for a Caucasian image dataset and an African-American dataset. Our results|suggest that research on demographic variation in accuracy|should include a check for balanced quality of the test data|as part of the problem formulation. This new understanding of the causes of the gender gap in recognition accuracy|will hopefully promote rational consideration of what might|be done about it. To promote reproducible research, matchers, attribute classifiers, and datasets used in this research are/will be publicly available.