{"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/a-comprehensive-survey-on-pose-invariant-face","title":"A Comprehensive Survey on Pose-Invariant Face Recognition","arxiv_id":"1502.04383","date":"2015-02-15","proceeding":null,"authors":["Changxing Ding","DaCheng Tao"],"abstract":"The capacity to recognize faces under varied poses is a fundamental human\nability that presents a unique challenge for computer vision systems. Compared\nto frontal face recognition, which has been intensively studied and has\ngradually matured in the past few decades, pose-invariant face recognition\n(PIFR) remains a largely unsolved problem. However, PIFR is crucial to\nrealizing the full potential of face recognition for real-world applications,\nsince face recognition is intrinsically a passive biometric technology for\nrecognizing uncooperative subjects. In this paper, we discuss the inherent\ndifficulties in PIFR and present a comprehensive review of established\ntechniques. Existing PIFR methods can be grouped into four categories, i.e.,\npose-robust feature extraction approaches, multi-view subspace learning\napproaches, face synthesis approaches, and hybrid approaches. The motivations,\nstrategies, pros/cons, and performance of representative approaches are\ndescribed and compared. Moreover, promising directions for future research are\ndiscussed.","url_abs":"http://arxiv.org/abs/1502.04383v3","url_pdf":"http://arxiv.org/pdf/1502.04383v3.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":"a-comprehensive-survey-on-pose-invariant-face","repo_url":"https://github.com/amanshenoy/pose-invariant-face-recognition","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"face-generation","task_name":"Face Generation"},{"task_slug":"face-recognition","task_name":"Face Recognition"},{"task_slug":"robust-face-recognition","task_name":"Robust Face Recognition"},{"task_slug":"survey","task_name":"Survey"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}