{"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/face-search-at-scale-80-million-gallery","title":"Face Search at Scale: 80 Million Gallery","arxiv_id":"1507.07242","date":"2015-07-26","proceeding":null,"authors":["Dayong Wang","Charles Otto","Anil K. Jain"],"abstract":"Due to the prevalence of social media websites, one challenge facing computer\nvision researchers is to devise methods to process and search for persons of\ninterest among the billions of shared photos on these websites. Facebook\nrevealed in a 2013 white paper that its users have uploaded more than 250\nbillion photos, and are uploading 350 million new photos each day. Due to this\nhumongous amount of data, large-scale face search for mining web images is both\nimportant and challenging. Despite significant progress in face recognition,\nsearching a large collection of unconstrained face images has not been\nadequately addressed. To address this challenge, we propose a face search\nsystem which combines a fast search procedure, coupled with a state-of-the-art\ncommercial off the shelf (COTS) matcher, in a cascaded framework. Given a probe\nface, we first filter the large gallery of photos to find the top-k most\nsimilar faces using deep features generated from a convolutional neural\nnetwork. The k candidates are re-ranked by combining similarities from deep\nfeatures and the COTS matcher. We evaluate the proposed face search system on a\ngallery containing 80 million web-downloaded face images. Experimental results\ndemonstrate that the deep features are competitive with state-of-the-art\nmethods on unconstrained face recognition benchmarks (LFW and IJB-A). Further,\nthe proposed face search system offers an excellent trade-off between accuracy\nand scalability on datasets consisting of millions of images. Additionally, in\nan experiment involving searching for face images of the Tsarnaev brothers,\nconvicted of the Boston Marathon bombing, the proposed face search system could\nfind the younger brother's (Dzhokhar Tsarnaev) photo at rank 1 in 1 second on a\n5M gallery and at rank 8 in 7 seconds on an 80M gallery.","url_abs":"http://arxiv.org/abs/1507.07242v2","url_pdf":"http://arxiv.org/pdf/1507.07242v2.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":"Deep CNN + COTS matcher","rank_in_archive_order":16,"of":17,"metrics":{"TAR @ FAR=0.01":"73.30%"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}