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@INPROCEEDINGS{Rahimi_IJCB_2023,
         author = {Rahimi, Parsa and Ecabert, Christophe and Marcel, S{\'{e}}bastien},
       keywords = {bias, Controlled Synthesis, Fairness, generative models, Synthetic Dataset},
       projects = {Idiap},
          title = {Toward responsible face datasets: modeling the distribution of a disentangled latent space for sampling face images from demographic groups},
      booktitle = {IEEE International Joint Conference on Biometrics},
           year = {2023},
       abstract = {Recently, it has been exposed that some modern facial recognition systems could discriminate specific demographic groups and may lead to unfair attention with respect to various facial attributes such as gender and origin. The main reason are the biases inside datasets, unbalanced demographics, used to train theses models. Unfortunately, collecting a large-scale balanced dataset with respect to various demographics is impracticable.
    In this paper, we investigate as an alternative the generation of a balanced and possibly bias-free synthetic dataset that could be used to train, to regularize or to evaluate deep learning-based facial recognition models. We propose to use a simple method for modeling and sampling a disentangled projection of a StyleGAN latent space to generate any combination of  demographic groups (e.g. $hispanic-female$). Our experiments show that we can synthesis any combination of demographic groups effectively and the identities are different from the original training dataset. We also released the source code \footnote{\url{https://gitlab.idiap.ch/biometric/sg_latent_modeling}}},
            pdf = {https://publications.idiap.ch/attachments/papers/2023/Rahimi_IJCB_2023.pdf}
}