{"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/cross-age-lfw-a-database-for-studying-cross","title":"Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments","arxiv_id":"1708.08197","date":"2017-08-28","proceeding":null,"authors":["Tianyue Zheng","Weihong Deng","Jiani Hu"],"abstract":"Labeled Faces in the Wild (LFW) database has been widely utilized as the\nbenchmark of unconstrained face verification and due to big data driven machine\nlearning methods, the performance on the database approaches nearly 100%.\nHowever, we argue that this accuracy may be too optimistic because of some\nlimiting factors. Besides different poses, illuminations, occlusions and\nexpressions, cross-age face is another challenge in face recognition. Different\nages of the same person result in large intra-class variations and aging\nprocess is unavoidable in real world face verification. However, LFW does not\npay much attention on it. Thereby we construct a Cross-Age LFW (CALFW) which\ndeliberately searches and selects 3,000 positive face pairs with age gaps to\nadd aging process intra-class variance. Negative pairs with same gender and\nrace are also selected to reduce the influence of attribute difference between\npositive/negative pairs and achieve face verification instead of attributes\nclassification. We evaluate several metric learning and deep learning methods\non the new database. Compared to the accuracy on LFW, the accuracy drops about\n10%-17% on CALFW.","url_abs":"http://arxiv.org/abs/1708.08197v1","url_pdf":"http://arxiv.org/pdf/1708.08197v1.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":"cross-age-lfw-a-database-for-studying-cross","repo_url":"https://github.com/Recognito-Vision/Linux-FaceRecognition-FaceLivenessDetection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"face-recognition","task_name":"Face Recognition"},{"task_slug":"face-verification","task_name":"Face Verification"},{"task_slug":"metric-learning","task_name":"Metric Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1708.08197","atlas_url":"https://app.syntology.ai/?focus=1708.08197","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}