{"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/occlusion-coherence-detecting-and-localizing","title":"Occlusion Coherence: Detecting and Localizing Occluded Faces","arxiv_id":"1506.08347","date":"2015-06-28","proceeding":null,"authors":["Golnaz Ghiasi","Charless C. Fowlkes"],"abstract":"The presence of occluders significantly impacts object recognition accuracy.\nHowever, occlusion is typically treated as an unstructured source of noise and\nexplicit models for occluders have lagged behind those for object appearance\nand shape. In this paper we describe a hierarchical deformable part model for\nface detection and landmark localization that explicitly models part occlusion.\nThe proposed model structure makes it possible to augment positive training\ndata with large numbers of synthetically occluded instances. This allows us to\neasily incorporate the statistics of occlusion patterns in a discriminatively\ntrained model. We test the model on several benchmarks for landmark\nlocalization and detection including challenging new data sets featuring\nsignificant occlusion. We find that the addition of an explicit occlusion model\nyields a detection system that outperforms existing approaches for occluded\ninstances while maintaining competitive accuracy in detection and landmark\nlocalization for unoccluded instances.","url_abs":"http://arxiv.org/abs/1506.08347v2","url_pdf":"http://arxiv.org/pdf/1506.08347v2.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":"occlusion-coherence-detecting-and-localizing","repo_url":"https://github.com/golnazghiasi/cofw68-benchmark","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"occlusion-coherence-detecting-and-localizing","repo_url":"https://github.com/golnazghiasi/hpm-detection-code","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"face-detection","task_name":"Face Detection"},{"task_slug":"object-recognition","task_name":"Object Recognition"},{"task_slug":"occluded-face-detection","task_name":"Occluded Face Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1506.08347","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}