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Although deep face recognition has achieved impressive progress in recentyears, controversy has arisen regarding discrimination based on skin tone,questioning their deployment into real-world scenarios. In this paper, we aimto systematically and scientifically study this bias from both data andalgorithm aspects. First, using the dermatologist approved Fitzpatrick SkinType classification system and Individual Typology Angle, we contribute abenchmark called Identity Shades (IDS) database, which effectively quantifiesthe degree of the bias with respect to skin tone in existing face recognitionalgorithms and commercial APIs. Further, we provide two skin-tone awaretraining datasets, called BUPT-Globalface dataset and BUPT-Balancedfacedataset, to remove bias in training data. Finally, to mitigate the algorithmicbias, we propose a novel meta-learning algorithm, called Meta Balanced Network(MBN), which learns adaptive margins in large margin loss such that the modeloptimized by this loss can perform fairly across people with different skintones. To determine the margins, our method optimizes a meta skewness loss on aclean and unbiased meta set and utilizes backward-on-backward automaticdifferentiation to perform a second order gradient descent step on the currentmargins. Extensive experiments show that MBN successfully mitigates bias andlearns more balanced performance for people with different skin tones in facerecognition. The proposed datasets are available athttp://www.whdeng.cn/RFW/index.html.

Meta Balanced Network for Fair Face Recognition