  Face recognition systems have significantly advanced in recent years, driven
by the availability of large-scale datasets. However, several issues have
recently came up, including privacy concerns that have led to the
discontinuation of well-established public datasets. Synthetic datasets have
emerged as a solution, even though current synthesis methods present other
drawbacks such as limited intra-class variations, lack of realism, and unfair
representation of demographic groups. This study introduces GANDiffFace, a
novel framework for the generation of synthetic datasets for face recognition
that combines the power of Generative Adversarial Networks (GANs) and Diffusion
models to overcome the limitations of existing synthetic datasets. In
GANDiffFace, we first propose the use of GANs to synthesize highly realistic
identities and meet target demographic distributions. Subsequently, we
fine-tune Diffusion models with the images generated with GANs, synthesizing
multiple images of the same identity with a variety of accessories, poses,
expressions, and contexts. We generate multiple synthetic datasets by changing
GANDiffFace settings, and compare their mated and non-mated score distributions
with the distributions provided by popular real-world datasets for face
recognition, i.e. VGG2 and IJB-C. Our results show the feasibility of the
proposed GANDiffFace, in particular the use of Diffusion models to enhance the
(limited) intra-class variations provided by GANs towards the level of
real-world datasets.
