{"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/do-we-really-need-to-collect-millions-of","title":"Do We Really Need to Collect Millions of Faces for Effective Face Recognition?","arxiv_id":"1603.07057","date":"2016-03-23","proceeding":null,"authors":["Iacopo Masi","Anh Tuan Tran","Jatuporn Toy Leksut","Tal Hassner","Gerard Medioni"],"abstract":"Face recognition capabilities have recently made extraordinary leaps. Though\nthis progress is at least partially due to ballooning training set sizes --\nhuge numbers of face images downloaded and labeled for identity -- it is not\nclear if the formidable task of collecting so many images is truly necessary.\nWe propose a far more accessible means of increasing training data sizes for\nface recognition systems. Rather than manually harvesting and labeling more\nfaces, we simply synthesize them. We describe novel methods of enriching an\nexisting dataset with important facial appearance variations by manipulating\nthe faces it contains. We further apply this synthesis approach when matching\nquery images represented using a standard convolutional neural network. The\neffect of training and testing with synthesized images is extensively tested on\nthe LFW and IJB-A (verification and identification) benchmarks and Janus CS2.\nThe performances obtained by our approach match state of the art results\nreported by systems trained on millions of downloaded images.","url_abs":"http://arxiv.org/abs/1603.07057v2","url_pdf":"http://arxiv.org/pdf/1603.07057v2.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":[],"tasks":[{"task_slug":"face-recognition","task_name":"Face Recognition"},{"task_slug":"face-verification","task_name":"Face Verification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/face-verification-on-ijb-a","task":"Face Verification","dataset":"IJB-A","model":"Synthesis as data augmentation","rank_in_archive_order":13,"of":17,"metrics":{"TAR @ FAR=0.01":"88.60%"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1603.07057","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}