Recent work from the Machine Learning and Computer Vision communities is
focusing on the use of Generative Adversarial Networks (GANs) for the
creation of synthetic face images with some level of control on semantic
factors (pose, expression, illumination, age, gender, …). However,
investigations on the use of these synthetic samples as a biometric
trait (face identities) are still lacking. The CITeR project LEGAL2 is a
continuation of the CITeR project LEGAL that will still be focused on
i-) the generation of synthetic biometric face datasets with a novel
approach and ii-) the usage of such datasets to train different Deep
Learning-based face recognition architectures and to benchmark face
recognition systems. The proposed approach aims to learn a mapping
within the StyleGAN latent space conditioned by semantic factors such
that the synthetized face minimizes both a reconstruction and an
identity loss.