{"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/unconstrained-face-alignment-via-cascaded","title":"Unconstrained Face Alignment via Cascaded Compositional Learning","arxiv_id":null,"date":"2016-06-01","proceeding":"CVPR 2016 6","authors":["Shizhan Zhu","Cheng Li","Chen-Change Loy","Xiaoou Tang"],"abstract":"We present a practical approach to address the problem of unconstrained face alignment for a single image. In our unconstrained problem, we need to deal with large shape and appearance variations under extreme head poses and rich shape deformation. To equip cascaded regressors with the capability to handle global shape variation and irregular appearance-shape relation in the unconstrained scenario, we partition the optimisation space into multiple domains of homogeneous descent, and predict a shape as a composition of estimations from multiple domain-specific regressors. With a specially formulated learning objective and a novel tree splitting function, our approach is capable of estimating a robust and meaningful composition. In addition to achieving state-of-the-art accuracy over existing approaches, our framework is also an efficient solution (350 FPS), thanks to the on-the-fly domain exclusion mechanism and the capability of leveraging the fast pixel feature.","url_abs":"http://openaccess.thecvf.com/content_cvpr_2016/html/Zhu_Unconstrained_Face_Alignment_CVPR_2016_paper.html","url_pdf":"http://openaccess.thecvf.com/content_cvpr_2016/papers/Zhu_Unconstrained_Face_Alignment_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":"face-alignment","task_name":"Face Alignment"}],"methods":[],"datasets_introduced":[{"slug":"aflw-19","name":"AFLW-19","full_name":"The 19 landmark variant of AFLW."}],"methods_introduced":[],"results":[{"leaderboard":"/sota/face-alignment-on-aflw-19","task":"Face Alignment","dataset":"AFLW-19","model":"CCL","rank_in_archive_order":19,"of":23,"metrics":{"NME_diag (%, Frontal)":"2.17","NME_diag (%, Full)":"2.72"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}