  We address the problem of bias in automated face recognition and demographic
attribute estimation algorithms, where errors are lower on certain cohorts
belonging to specific demographic groups. We present a novel de-biasing
adversarial network (DebFace) that learns to extract disentangled feature
representations for both unbiased face recognition and demographics estimation.
The proposed network consists of one identity classifier and three demographic
classifiers (for gender, age, and race) that are trained to distinguish
identity and demographic attributes, respectively. Adversarial learning is
adopted to minimize correlation among feature factors so as to abate bias
influence from other factors. We also design a new scheme to combine
demographics with identity features to strengthen robustness of face
representation in different demographic groups. The experimental results show
that our approach is able to reduce bias in face recognition as well as
demographics estimation while achieving state-of-the-art performance.
