  State-of-the-art face recognition systems require vast amounts of labeled
training data. Given the priority of privacy in face recognition applications,
the data is limited to celebrity web crawls, which have issues such as limited
numbers of identities. On the other hand, self-supervised revolution in the
industry motivates research on the adaptation of related techniques to facial
recognition. One of the most popular practical tricks is to augment the dataset
by the samples drawn from generative models while preserving the identity. We
show that a simple approach based on fine-tuning pSp encoder for StyleGAN
allows us to improve upon the state-of-the-art facial recognition and performs
better compared to training on synthetic face identities. We also collect
large-scale unlabeled datasets with controllable ethnic constitution --
AfricanFaceSet-5M (5 million images of different people) and AsianFaceSet-3M (3
million images of different people) -- and we show that pretraining on each of
them improves recognition of the respective ethnicities (as well as others),
while combining all unlabeled datasets results in the biggest performance
increase. Our self-supervised strategy is the most useful with limited amounts
of labeled training data, which can be beneficial for more tailored face
recognition tasks and when facing privacy concerns. Evaluation is based on a
standard RFW dataset and a new large-scale RB-WebFace benchmark. The code and
data are made publicly available at
https://github.com/seva100/stylegan-for-facerec.
