{"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/latent-factor-guided-convolutional-neural","title":"Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition","arxiv_id":null,"date":"2016-06-01","proceeding":"CVPR 2016 6","authors":["Yandong Wen","Zhifeng Li","Yu Qiao"],"abstract":"While considerable progresses have been made on face recognition, age-invariant face recognition (AIFR) still remains a major challenge in real world applications of face recognition systems. The major difficulty of AIFR arises from the fact that the facial appearance is subject to significant intra-personal changes caused by the aging process over time. In order to address this problem, we propose a novel deep face recognition framework to learn the age-invariant deep face features through a carefully designed CNN model. To the best of our knowledge, this is the first attempt to show the effectiveness of deep CNNs in advancing the state-of-the-art of AIFR. Extensive experiments are conducted on several public domain face aging datasets (MORPH Album2, FGNET, and CACD-VS) to demonstrate the effectiveness of the proposed model over the state-of-the-art. We also verify the excellent generalization of our new model on the famous LFW dataset.","url_abs":"http://openaccess.thecvf.com/content_cvpr_2016/html/Wen_Latent_Factor_Guided_CVPR_2016_paper.html","url_pdf":"http://openaccess.thecvf.com/content_cvpr_2016/papers/Wen_Latent_Factor_Guided_CVPR_2016_paper.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":"age-invariant-face-recognition","task_name":"Age-Invariant Face Recognition"},{"task_slug":"face-recognition","task_name":"Face Recognition"},{"task_slug":"morph","task_name":"MORPH"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/age-invariant-face-recognition-on-cacdvs","task":"Age-Invariant Face Recognition","dataset":"CACDVS","model":"LF-CNNs","rank_in_archive_order":7,"of":9,"metrics":{"Accuracy":"98.5"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}