  In this paper we investigate the feasibility of using synthetic data to
augment face datasets. In particular, we propose a novel generative adversarial
network (GAN) that can disentangle identity-related attributes from
non-identity-related attributes. This is done by training an embedding network
that maps discrete identity labels to an identity latent space that follows a
simple prior distribution, and training a GAN conditioned on samples from that
distribution. Our proposed GAN allows us to augment face datasets by generating
both synthetic images of subjects in the training set and synthetic images of
new subjects not in the training set. By using recent advances in GAN training,
we show that the synthetic images generated by our model are photo-realistic,
and that training with augmented datasets can indeed increase the accuracy of
face recognition models as compared with models trained with real images alone.
