Generative models using deep learning are heavily researched nowadays by
both Machine Learning and Computer Vision communities. The generation
of synthetic data linked with biometrics activity mostly covers the
generation of random faces by either using GANs or VAEs with slight
control on some semantic factors. However, the consideration of those
synthetic samples as a biometric trait (face identities) is neglected by
the scientific community. The CITeR project LEGAL is focused on i) the
generation of synthetic biometric face datasets and ii) the usage of
such datasets to reliably train and benchmark face recognition systems.