On the use of automatically generated synthetic image datasets for benchmarking face recognition

• URL: http://arxiv.org/abs/2106.04215v1
• Date: Tue, 8 Jun 2021 09:54:02 GMT
• Title: On the use of automatically generated synthetic image datasets for benchmarking face recognition
• Authors: Laurent Colbois, Tiago de Freitas Pereira and S\'ebastien Marcel
• Abstract summary: Recent advances in Generative Adversarial Networks (GANs) provide a pathway to replace real datasets by synthetic datasets. Recent advances in Generative Adversarial Networks (GANs) to synthesize realistic face images provide a pathway to replace real datasets by synthetic datasets. 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.
• Score: 2.0196229393131726
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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