{"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/sspp-dan-deep-domain-adaptation-network-for","title":"SSPP-DAN: Deep Domain Adaptation Network for Face Recognition with Single Sample Per Person","arxiv_id":"1702.04069","date":"2017-02-14","proceeding":null,"authors":["Sungeun Hong","Woobin Im","Jongbin Ryu","Hyun S. Yang"],"abstract":"Real-world face recognition using a single sample per person (SSPP) is a\nchallenging task. The problem is exacerbated if the conditions under which the\ngallery image and the probe set are captured are completely different. To\naddress these issues from the perspective of domain adaptation, we introduce an\nSSPP domain adaptation network (SSPP-DAN). In the proposed approach, domain\nadaptation, feature extraction, and classification are performed jointly using\na deep architecture with domain-adversarial training. However, the SSPP\ncharacteristic of one training sample per class is insufficient to train the\ndeep architecture. To overcome this shortage, we generate synthetic images with\nvarying poses using a 3D face model. Experimental evaluations using a realistic\nSSPP dataset show that deep domain adaptation and image synthesis complement\neach other and dramatically improve accuracy. Experiments on a benchmark\ndataset using the proposed approach show state-of-the-art performance. All the\ndataset and the source code can be found in our online repository\n(https://github.com/csehong/SSPP-DAN).","url_abs":"http://arxiv.org/abs/1702.04069v4","url_pdf":"http://arxiv.org/pdf/1702.04069v4.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":"sspp-dan-deep-domain-adaptation-network-for","repo_url":"https://github.com/csehong/SSPP-DAN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"face-model","task_name":"Face Model"},{"task_slug":"face-recognition","task_name":"Face Recognition"},{"task_slug":"image-generation","task_name":"Image Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1702.04069","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}