  Although significant progress has been made in face recognition, demographic
bias still exists in face recognition systems. For instance, it usually happens
that the face recognition performance for a certain demographic group is lower
than the others. In this paper, we propose MixFairFace framework to improve the
fairness in face recognition models. First of all, we argue that the commonly
used attribute-based fairness metric is not appropriate for face recognition. A
face recognition system can only be considered fair while every person has a
close performance. Hence, we propose a new evaluation protocol to fairly
evaluate the fairness performance of different approaches. Different from
previous approaches that require sensitive attribute labels such as race and
gender for reducing the demographic bias, we aim at addressing the identity
bias in face representation, i.e., the performance inconsistency between
different identities, without the need for sensitive attribute labels. To this
end, we propose MixFair Adapter to determine and reduce the identity bias of
training samples. Our extensive experiments demonstrate that our MixFairFace
approach achieves state-of-the-art fairness performance on all benchmark
datasets.
