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 [BibTeX] [Marc21]
On the use of automatically generated synthetic image datasets for benchmarking face recognition
Type of publication: Conference paper
Citation: Colbois_IJCB_2021
Publication status: Accepted
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.
Projects Idiap
Biometrics Center
Authors Colbois, Laurent
de Freitas Pereira, Tiago
Marcel, S├ębastien
Added by: [UNK]
Total mark: 0
  • Colbois_IJCB_2021.pdf