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@INPROCEEDINGS{Colbois_IJCB_2021,
         author = {Colbois, Laurent and de Freitas Pereira, Tiago and Marcel, S{\'{e}}bastien},
       projects = {Idiap, Biometrics Center},
          title = {On the use of automatically generated synthetic image datasets for benchmarking face recognition},
      booktitle = {International Joint Conference on Biometrics (IJCB 2021)},
           year = {2021},
           note = {Accepted for Publication in IJCB2021},
       abstract = {The availability of large-scale face datasets has been key in the progress of face recognition. However, due to licensing issues or copyright infringement, some datasets are not available anymore (e.g. MS-Celeb-1M). Recent advances in Generative Adversarial Networks (GANs), to synthesize realistic face images, provide a pathway to replace real datasets by synthetic datasets, both to train and benchmark face recognition (FR) systems. The work presented in this paper provides a study on benchmarking FR systems using
a synthetic dataset. First, we introduce the proposed methodology to generate a synthetic dataset, without the need for human intervention, by exploiting the latent structure of a StyleGAN2 model with multiple controlled factors of variation. Then, we confirm that (i) the generated synthetic identities are not data subjects from the GAN's training dataset, which is verified on a synthetic dataset with 10K+ identities; (ii) benchmarking results on the synthetic dataset are a good substitution, often providing error rates and system ranking similar to the benchmarking on the real dataset.},
            pdf = {https://publications.idiap.ch/attachments/papers/2021/Colbois_IJCB_2021.pdf}
}