  The availability of large-scale face datasets has been key in the progress of
face recognition. However, due to licensing issues or copyright infringement,
some datasets are not available anymore (e.g. MS-Celeb-1M). Recent advances in
Generative Adversarial Networks (GANs), to synthesize realistic face images,
provide a pathway to replace real datasets by synthetic datasets, both to train
and benchmark face recognition (FR) systems. The work presented in this paper
provides a study on benchmarking FR systems using a synthetic dataset. First,
we introduce the proposed methodology to generate a synthetic dataset, without
the need for human intervention, by exploiting the latent structure of a
StyleGAN2 model with multiple controlled factors of variation. Then, we confirm
that (i) the generated synthetic identities are not data subjects from the
GAN's training dataset, which is verified on a synthetic dataset with 10K+
identities; (ii) benchmarking results on the synthetic dataset are a good
substitution, often providing error rates and system ranking similar to the
benchmarking on the real dataset.
