  Racial bias is an important issue in biometric, but has not been thoroughly
studied in deep face recognition. In this paper, we first contribute a
dedicated dataset called Racial Faces in-the-Wild (RFW) database, on which we
firmly validated the racial bias of four commercial APIs and four
state-of-the-art (SOTA) algorithms. Then, we further present the solution using
deep unsupervised domain adaptation and propose a deep information maximization
adaptation network (IMAN) to alleviate this bias by using Caucasian as source
domain and other races as target domains. This unsupervised method
simultaneously aligns global distribution to decrease race gap at domain-level,
and learns the discriminative target representations at cluster level. A novel
mutual information loss is proposed to further enhance the discriminative
ability of network output without label information. Extensive experiments on
RFW, GBU, and IJB-A databases show that IMAN successfully learns features that
generalize well across different races and across different databases.
