  We propose a discrimination-aware learning method to improve both accuracy
and fairness of biased face recognition algorithms. The most popular face
recognition benchmarks assume a distribution of subjects without paying much
attention to their demographic attributes. In this work, we perform a
comprehensive discrimination-aware experimentation of deep learning-based face
recognition. We also propose a general formulation of algorithmic
discrimination with application to face biometrics. The experiments include
tree popular face recognition models and three public databases composed of
64,000 identities from different demographic groups characterized by gender and
ethnicity. We experimentally show that learning processes based on the most
used face databases have led to popular pre-trained deep face models that
present a strong algorithmic discrimination. We finally propose a
discrimination-aware learning method, Sensitive Loss, based on the popular
triplet loss function and a sensitive triplet generator. Our approach works as
an add-on to pre-trained networks and is used to improve their performance in
terms of average accuracy and fairness. The method shows results comparable to
state-of-the-art de-biasing networks and represents a step forward to prevent
discriminatory effects by automatic systems.
