{"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/face-alignment-by-coarse-to-fine-shape-1","title":"Face alignment by coarse-to-fine shape searching","arxiv_id":null,"date":"2015-06-07","proceeding":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2015 6","authors":["Shizhan Zhu","Cheng Li","Chen Change Loy","Xiaoou Tang"],"abstract":"We present a novel face alignment framework based on coarse-to-fine shape searching. Unlike the conventional cascaded regression approaches that start with an initial shape and refine the shape in a cascaded manner, our approach begins with a coarse search over a shape space that contains diverse shapes, and employs the coarse solution to constrain subsequent finer search of shapes. The unique stage-by-stage progressive and adaptive search i) prevents the final solution from being trapped in local optima due to poor initialisation, a common problem encountered by cascaded regression approaches; and ii) improves the robustness in coping with large pose variations. The framework demonstrates real-time performance and state-of-the-art results on various benchmarks including the challenging 300-W dataset.","url_abs":"https://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Zhu_Face_Alignment_by_2015_CVPR_paper.html","url_pdf":"https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Zhu_Face_Alignment_by_2015_CVPR_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":[{"paper_slug":"face-alignment-by-coarse-to-fine-shape-1","repo_url":"https://github.com/zhusz/CVPR15-CFSS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"face-alignment","task_name":"Face Alignment"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/face-alignment-on-aflw-19","task":"Face Alignment","dataset":"AFLW-19","model":"CFSS","rank_in_archive_order":20,"of":23,"metrics":{"NME_diag (%, Frontal)":"2.68","NME_diag (%, Full)":"3.92"},"uses_additional_data":false},{"leaderboard":"/sota/face-alignment-on-wflw","task":"Face Alignment","dataset":"WFLW","model":"CFSS","rank_in_archive_order":31,"of":36,"metrics":{"AUC@10 (inter-ocular)":"36.6","FR@10 (inter-ocular)":"20.56","NME (inter-ocular)":"9.07"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}