{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/on-the-use-of-automatically-generated","title":"On the use of automatically generated synthetic image datasets for benchmarking face recognition","arxiv_id":"2106.04215","date":"2021-06-08","proceeding":null,"authors":["Laurent Colbois","Tiago de Freitas Pereira","Sébastien Marcel"],"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.","url_abs":"https://arxiv.org/abs/2106.04215v1","url_pdf":"https://arxiv.org/pdf/2106.04215v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"on-the-use-of-automatically-generated","repo_url":"https://gitlab.idiap.ch/bob/bob.paper.ijcb2021_synthetic_dataset","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"face-recognition","task_name":"Face Recognition"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"path-length-regularization","method_name":"Path Length Regularization"},{"method_slug":"r1-regularization","method_name":"R1 Regularization"},{"method_slug":"weight-demodulation","method_name":"Weight Demodulation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2106.04215","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}