{"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-deeply-initialized-coarse-to-fine-ensemble","title":"A Deeply-initialized Coarse-to-fine Ensemble of Regression Trees for Face Alignment","arxiv_id":null,"date":"2018-09-01","proceeding":"ECCV 2018 9","authors":["Roberto Valle","Jose M. Buenaposada","Antonio Valdes","Luis Baumela"],"abstract":"In this paper we present DCFE, a real-time facial landmark regression method based on a coarse-to-fine Ensemble of Regression Trees (ERT). We use a simple Convolutional Neural Network (CNN) to generate probability maps of landmarks location. These are further refined with the ERT regressor, which is initialized by fitting a 3D face model to the landmark maps. The coarse-to-fine structure of the ERT lets us address the combinatorial explosion of parts deformation. With the 3D model we also tackle other key problems such as robust regressor initialization, self occlusions, and simultaneous frontal and profile face analysis. In the experiments DCFE achieves the best reported result in AFLW, COFW, and 300W private and common public data sets.","url_abs":"http://openaccess.thecvf.com/content_ECCV_2018/html/Roberto_Valle_A_Deeply-initialized_Coarse-to-fine_ECCV_2018_paper.html","url_pdf":"http://openaccess.thecvf.com/content_ECCV_2018/papers/Roberto_Valle_A_Deeply-initialized_Coarse-to-fine_ECCV_2018_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":"a-deeply-initialized-coarse-to-fine-ensemble","repo_url":"https://github.com/bobetocalo/bobetocalo_eccv18","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"face-alignment","task_name":"Face Alignment"},{"task_slug":"face-model","task_name":"Face Model"},{"task_slug":"facial-landmark-detection","task_name":"Facial Landmark Detection"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/face-alignment-on-300w","task":"Face Alignment","dataset":"300W","model":"DCFE","rank_in_archive_order":19,"of":48,"metrics":{"NME_inter-ocular (%, Challenge)":"5.22","NME_inter-ocular (%, Common)":"2.76","NME_inter-ocular (%, Full)":"3.24","NME_inter-pupil (%, Challenge)":"7.54","NME_inter-pupil (%, Common)":"3.83","NME_inter-pupil (%, Full)":"4.55"},"uses_additional_data":false},{"leaderboard":"/sota/face-alignment-on-300w-split-2","task":"Face Alignment","dataset":"300W Split 2","model":"DCFE","rank_in_archive_order":6,"of":7,"metrics":{"AUC@8 (inter-ocular)":"52.42","FR@8 (inter-ocular)":"1.83","NME (inter-ocular)":"3.88"},"uses_additional_data":false},{"leaderboard":"/sota/face-alignment-on-cofw","task":"Face Alignment","dataset":"COFW","model":"DCFE","rank_in_archive_order":26,"of":28,"metrics":{"NME (inter-pupil)":"5.27%"},"uses_additional_data":false},{"leaderboard":"/sota/face-alignment-on-ibug","task":"Face Alignment","dataset":"IBUG","model":"DCFE (inter pupils normalization)","rank_in_archive_order":2,"of":2,"metrics":{"Mean Error Rate":"7.54%"},"uses_additional_data":false},{"leaderboard":"/sota/facial-landmark-detection-on-300w","task":"Facial Landmark Detection","dataset":"300W","model":"DCFE (Inter-ocular Norm)","rank_in_archive_order":6,"of":15,"metrics":{"NME":"3.24"},"uses_additional_data":false},{"leaderboard":"/sota/facial-landmark-detection-on-aflw-full","task":"Facial Landmark Detection","dataset":"AFLW-Full","model":"DCFE (Box height Norm, 19 landmarks - no earlobs)","rank_in_archive_order":4,"of":5,"metrics":{"Mean NME ":"2.17"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}