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Racial bias is an important issue in biometric, but has not been thoroughlystudied in deep face recognition. In this paper, we first contribute adedicated dataset called Racial Faces in-the-Wild (RFW) database, on which wefirmly validated the racial bias of four commercial APIs and fourstate-of-the-art (SOTA) algorithms. Then, we further present the solution usingdeep unsupervised domain adaptation and propose a deep information maximizationadaptation network (IMAN) to alleviate this bias by using Caucasian as sourcedomain and other races as target domains. This unsupervised methodsimultaneously aligns global distribution to decrease race gap at domain-level,and learns the discriminative target representations at cluster level. A novelmutual information loss is proposed to further enhance the discriminativeability of network output without label information. Extensive experiments onRFW, GBU, and IJB-A databases show that IMAN successfully learns features thatgeneralize well across different races and across different databases.

Racial Faces in-the-Wild: Reducing Racial Bias by Information Maximization Adaptation Network