  The most popular face recognition benchmarks assume a distribution of
subjects without much attention to their demographic attributes. In this work,
we perform a comprehensive discrimination-aware experimentation of deep
learning-based face recognition. The main aim of this study is focused on a
better understanding of the feature space generated by deep models, and the
performance achieved over different demographic groups. We also propose a
general formulation of algorithmic discrimination with application to face
biometrics. The experiments are conducted over the new DiveFace database
composed of 24K identities from six different demographic groups. Two popular
face recognition models are considered in the experimental framework: ResNet-50
and VGG-Face. We experimentally show that demographic groups highly represented
in popular face databases have led to popular pre-trained deep face models
presenting strong algorithmic discrimination. That discrimination can be
observed both qualitatively at the feature space of the deep models and
quantitatively in large performance differences when applying those models in
different demographic groups, e.g. for face biometrics.
