{"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/combining-data-driven-and-model-driven","title":"Combining Data-driven and Model-driven Methods for Robust Facial Landmark Detection","arxiv_id":"1611.10152","date":"2016-11-30","proceeding":null,"authors":["Hongwen Zhang","Qi Li","Zhenan Sun","Yunfan Liu"],"abstract":"Facial landmark detection is an important yet challenging task for real-world\ncomputer vision applications. This paper proposes an effective and robust\napproach for facial landmark detection by combining data- and model-driven\nmethods. Firstly, a Fully Convolutional Network (FCN) is trained to compute\nresponse maps of all facial landmark points. Such a data-driven method could\nmake full use of holistic information in a facial image for global estimation\nof facial landmarks. After that, the maximum points in the response maps are\nfitted with a pre-trained Point Distribution Model (PDM) to generate the\ninitial facial shape. This model-driven method is able to correct the\ninaccurate locations of outliers by considering the shape prior information.\nFinally, a weighted version of Regularized Landmark Mean-Shift (RLMS) is\nemployed to fine-tune the facial shape iteratively. This\nEstimation-Correction-Tuning process perfectly combines the advantages of the\nglobal robustness of data-driven method (FCN), outlier correction capability of\nmodel-driven method (PDM) and non-parametric optimization of RLMS. Results of\nextensive experiments demonstrate that our approach achieves state-of-the-art\nperformances on challenging datasets including 300W, AFLW, AFW and COFW. The\nproposed method is able to produce satisfying detection results on face images\nwith exaggerated expressions, large head poses, and partial occlusions.","url_abs":"http://arxiv.org/abs/1611.10152v2","url_pdf":"http://arxiv.org/pdf/1611.10152v2.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":"combining-data-driven-and-model-driven","repo_url":"https://github.com/HongwenZhang/ECT-FaceAlignment","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"facial-landmark-detection","task_name":"Facial Landmark Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}