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. |
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| Projects |
Idiap Biometrics Center |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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