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Recently, it has been exposed that some modern facial recognition systemscould discriminate specific demographic groups and may lead to unfair attentionwith respect to various facial attributes such as gender and origin. The mainreason are the biases inside datasets, unbalanced demographics, used to traintheses models. Unfortunately, collecting a large-scale balanced dataset withrespect to various demographics is impracticable.  In this paper, we investigate as an alternative the generation of a balancedand possibly bias-free synthetic dataset that could be used to train, toregularize or to evaluate deep learning-based facial recognition models. Wepropose to use a simple method for modeling and sampling a disentangledprojection of a StyleGAN latent space to generate any combination ofdemographic groups (e.g. $hispanic-female$). Our experiments show that we cansynthesis any combination of demographic groups effectively and the identitiesare different from the original training dataset. We also released the sourcecode.

Toward responsible face datasets: modeling the distribution of a disentangled latent space for sampling face images from demographic groups