Toward responsible face datasets: modeling the distribution of a
  disentangled latent space for sampling face images from demographic groups
Parsa Rahimi Christophe Ecabert Sebastien Marcel
Recently, it has been exposed that some modern facial recognition systems
could discriminate specific demographic groups and may lead to unfair attention
with respect to various facial attributes such as gender and origin. The main
reason are the biases inside datasets, unbalanced demographics, used to train
theses models. Unfortunately, collecting a large-scale balanced dataset with
respect to various demographics is impracticable.
  In this paper, we investigate as an alternative the generation of a balanced
and possibly bias-free synthetic dataset that could be used to train, to
regularize or to evaluate deep learning-based facial recognition models. We
propose to use a simple method for modeling and sampling a disentangled
projection of a StyleGAN latent space to generate any combination of
demographic groups (e.g. $hispanic-female$). Our experiments show that we can
synthesis any combination of demographic groups effectively and the identities
are different from the original training dataset. We also released the source
code.