  Many of the commonly used datasets for face recognition development are
collected from the internet without proper user consent. Due to the increasing
focus on privacy in the social and legal frameworks, the use and distribution
of these datasets are being restricted and strongly questioned. These
databases, which have a realistically high variability of data per identity,
have enabled the success of face recognition models. To build on this success
and to align with privacy concerns, synthetic databases, consisting purely of
synthetic persons, are increasingly being created and used in the development
of face recognition solutions. In this work, we present a three-player
generative adversarial network (GAN) framework, namely IDnet, that enables the
integration of identity information into the generation process. The third
player in our IDnet aims at forcing the generator to learn to generate
identity-separable face images. We empirically proved that our IDnet synthetic
images are of higher identity discrimination in comparison to the conventional
two-player GAN, while maintaining a realistic intra-identity variation. We
further studied the identity link between the authentic identities used to
train the generator and the generated synthetic identities, showing very low
similarities between these identities. We demonstrated the applicability of our
IDnet data in training face recognition models by evaluating these models on a
wide set of face recognition benchmarks. In comparison to the state-of-the-art
works in synthetic-based face recognition, our solution achieved comparable
results to a recent rendering-based approach and outperformed all existing
GAN-based approaches. The training code and the synthetic face image dataset
are publicly available ( https://github.com/fdbtrs/Synthetic-Face-Recognition
).
