{"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-using-a-3d-deeply-initialized","title":"Face Alignment using a 3D Deeply-initialized Ensemble of Regression Trees","arxiv_id":"1902.01831","date":"2019-02-05","proceeding":null,"authors":["Roberto Valle","José M. Buenaposada","Antonio Valdés","Luis Baumela"],"abstract":"Face alignment algorithms locate a set of landmark points in images of faces taken in unrestricted situations. State-of-the-art approaches typically fail or lose accuracy in the presence of occlusions, strong deformations, large pose variations and ambiguous configurations. In this paper we present 3DDE, a robust and efficient face alignment algorithm based on a coarse-to-fine cascade of ensembles of regression trees. It is initialized by robustly fitting a 3D face model to the probability maps produced by a convolutional neural network. With this initialization we address self-occlusions and large face rotations. Further, the regressor implicitly imposes a prior face shape on the solution, addressing occlusions and ambiguous face configurations. Its coarse-to-fine structure tackles the combinatorial explosion of parts deformation. In the experiments performed, 3DDE improves the state-of-the-art in 300W, COFW, AFLW and WFLW data sets. Finally, we perform cross-dataset experiments that reveal the existence of a significant data set bias in these benchmarks.","url_abs":"https://arxiv.org/abs/1902.01831v2","url_pdf":"https://arxiv.org/pdf/1902.01831v2.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-using-a-3d-deeply-initialized","repo_url":"https://github.com/bobetocalo/bobetocalo_eccv18","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"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":"3DDE","rank_in_archive_order":15,"of":48,"metrics":{"NME_inter-ocular (%, Challenge)":"4.92","NME_inter-ocular (%, Common)":"2.69","NME_inter-ocular (%, Full)":"3.13","NME_inter-pupil (%, Challenge)":"7.10","NME_inter-pupil (%, Common)":"3.73","NME_inter-pupil (%, Full)":"4.39"},"uses_additional_data":false},{"leaderboard":"/sota/face-alignment-on-300w-split-2","task":"Face Alignment","dataset":"300W Split 2","model":"3DDE","rank_in_archive_order":5,"of":7,"metrics":{"AUC@8 (inter-ocular)":"53.94","FR@8 (inter-ocular)":"2.33","NME (inter-ocular)":"3.73"},"uses_additional_data":false},{"leaderboard":"/sota/face-alignment-on-cofw","task":"Face Alignment","dataset":"COFW","model":"3DDE (Inter-pupil Norm)","rank_in_archive_order":24,"of":28,"metrics":{"NME (inter-pupil)":"5.11%","Recall at 80% precision (Landmarks Visibility)":"63.89"},"uses_additional_data":false},{"leaderboard":"/sota/face-alignment-on-wflw","task":"Face Alignment","dataset":"WFLW","model":"3DDE","rank_in_archive_order":26,"of":36,"metrics":{"AUC@10 (inter-ocular)":"55.44","FR@10 (inter-ocular)":"5.04","NME (inter-ocular)":"4.68"},"uses_additional_data":false},{"leaderboard":"/sota/facial-landmark-detection-on-300w","task":"Facial Landmark Detection","dataset":"300W","model":"3DDE (Inter-ocular Norm)","rank_in_archive_order":5,"of":15,"metrics":{"NME":"3.13"},"uses_additional_data":false},{"leaderboard":"/sota/facial-landmark-detection-on-aflw-full","task":"Facial Landmark Detection","dataset":"AFLW-Full","model":"3DDE (Box height Norm, 19 landmarks - no earlobs)","rank_in_archive_order":5,"of":5,"metrics":{"Mean NME":"2.01"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}