  Face recognition and verification are two computer vision tasks whose
performance has progressed with the introduction of deep representations.
However, ethical, legal, and technical challenges due to the sensitive
character of face data and biases in real training datasets hinder their
development. Generative AI addresses privacy by creating fictitious identities,
but fairness problems persist. We promote fairness by introducing a demographic
attributes balancing mechanism in generated training datasets. We experiment
with an existing real dataset, three generated training datasets, and the
balanced versions of a diffusion-based dataset. We propose a comprehensive
evaluation that considers accuracy and fairness equally and includes a rigorous
regression-based statistical analysis of attributes. The analysis shows that
balancing reduces demographic unfairness. Also, a performance gap persists
despite generation becoming more accurate with time. The proposed balancing
method and comprehensive verification evaluation promote fairer and transparent
face recognition and verification.
