{"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/digiface-1m-1-million-digital-face-images-for","title":"DigiFace-1M: 1 Million Digital Face Images for Face Recognition","arxiv_id":"2210.02579","date":"2022-10-05","proceeding":null,"authors":["Gwangbin Bae","Martin de La Gorce","Tadas Baltrusaitis","Charlie Hewitt","Dong Chen","Julien Valentin","Roberto Cipolla","Jingjing Shen"],"abstract":"State-of-the-art face recognition models show impressive accuracy, achieving over 99.8% on Labeled Faces in the Wild (LFW) dataset. Such models are trained on large-scale datasets that contain millions of real human face images collected from the internet. Web-crawled face images are severely biased (in terms of race, lighting, make-up, etc) and often contain label noise. More importantly, the face images are collected without explicit consent, raising ethical concerns. To avoid such problems, we introduce a large-scale synthetic dataset for face recognition, obtained by rendering digital faces using a computer graphics pipeline. We first demonstrate that aggressive data augmentation can significantly reduce the synthetic-to-real domain gap. Having full control over the rendering pipeline, we also study how each attribute (e.g., variation in facial pose, accessories and textures) affects the accuracy. Compared to SynFace, a recent method trained on GAN-generated synthetic faces, we reduce the error rate on LFW by 52.5% (accuracy from 91.93% to 96.17%). By fine-tuning the network on a smaller number of real face images that could reasonably be obtained with consent, we achieve accuracy that is comparable to the methods trained on millions of real face images.","url_abs":"https://arxiv.org/abs/2210.02579v1","url_pdf":"https://arxiv.org/pdf/2210.02579v1.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":"digiface-1m-1-million-digital-face-images-for","repo_url":"https://github.com/microsoft/digiface1m","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"face-recognition","task_name":"Face Recognition"},{"task_slug":"synthetic-data-generation","task_name":"Synthetic Data Generation"},{"task_slug":"synthetic-face-recognition","task_name":"Synthetic Face Recognition"}],"methods":[],"datasets_introduced":[{"slug":"digiface-1m","name":"DigiFace-1M","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/synthetic-face-recognition-on-agedb-30","task":"Synthetic Face Recognition","dataset":"AgeDB-30","model":"DigiFace-1M","rank_in_archive_order":3,"of":3,"metrics":{"Accuracy":"0.811"},"uses_additional_data":false},{"leaderboard":"/sota/synthetic-face-recognition-on-calfw","task":"Synthetic Face Recognition","dataset":"CALFW","model":"DigiFace-1M","rank_in_archive_order":3,"of":3,"metrics":{"Accuracy":"0.8255"},"uses_additional_data":false},{"leaderboard":"/sota/synthetic-face-recognition-on-cfp-fp","task":"Synthetic Face Recognition","dataset":"CFP-FP","model":"DigiFace-1M","rank_in_archive_order":2,"of":3,"metrics":{"Accuracy":"0.8981"},"uses_additional_data":false},{"leaderboard":"/sota/synthetic-face-recognition-on-cplfw","task":"Synthetic Face Recognition","dataset":"CPLFW","model":"DigiFace-1M","rank_in_archive_order":2,"of":3,"metrics":{"Accuracy":"0.8223"},"uses_additional_data":false},{"leaderboard":"/sota/synthetic-face-recognition-on-lfw","task":"Synthetic Face Recognition","dataset":"LFW","model":"DigiFace-1M","rank_in_archive_order":3,"of":3,"metrics":{"Accuracy":"0.9617"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2210.02579","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}