{"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/deepvisage-making-face-recognition-simple-yet","title":"DeepVisage: Making face recognition simple yet with powerful generalization skills","arxiv_id":"1703.08388","date":"2017-03-24","proceeding":null,"authors":["Abul Hasnat","Julien Bohné","Jonathan Milgram","Stéphane Gentric","Liming Chen"],"abstract":"Face recognition (FR) methods report significant performance by adopting the\nconvolutional neural network (CNN) based learning methods. Although CNNs are\nmostly trained by optimizing the softmax loss, the recent trend shows an\nimprovement of accuracy with different strategies, such as task-specific CNN\nlearning with different loss functions, fine-tuning on target dataset, metric\nlearning and concatenating features from multiple CNNs. Incorporating these\ntasks obviously requires additional efforts. Moreover, it demotivates the\ndiscovery of efficient CNN models for FR which are trained only with identity\nlabels. We focus on this fact and propose an easily trainable and single CNN\nbased FR method. Our CNN model exploits the residual learning framework.\nAdditionally, it uses normalized features to compute the loss. Our extensive\nexperiments show excellent generalization on different datasets. We obtain very\ncompetitive and state-of-the-art results on the LFW, IJB-A, YouTube faces and\nCACD datasets.","url_abs":"http://arxiv.org/abs/1703.08388v2","url_pdf":"http://arxiv.org/pdf/1703.08388v2.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":"metric-learning","task_name":"Metric Learning"}],"methods":[{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/age-invariant-face-recognition-on-cacdvs","task":"Age-Invariant Face Recognition","dataset":"CACDVS","model":"DeepVisage","rank_in_archive_order":6,"of":9,"metrics":{"Accuracy":"99.13%"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1703.08388","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}