{"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/fddb-360-face-detection-in-360-degree-fisheye","title":"FDDB-360: Face Detection in 360-degree Fisheye Images","arxiv_id":"1902.02777","date":"2019-02-07","proceeding":null,"authors":["Jianglin Fu","Saeed Ranjbar Alvar","Ivan V. Bajic","Rodney G. Vaughan"],"abstract":"360-degree cameras offer the possibility to cover a large area, for example\nan entire room, without using multiple distributed vision sensors. However,\ngeometric distortions introduced by their lenses make computer vision problems\nmore challenging. In this paper we address face detection in 360-degree fisheye\nimages. We show how a face detector trained on regular images can be re-trained\nfor this purpose, and we also provide a 360-degree fisheye-like version of the\npopular FDDB face detection dataset, which we call FDDB-360.","url_abs":"http://arxiv.org/abs/1902.02777v1","url_pdf":"http://arxiv.org/pdf/1902.02777v1.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-detection","task_name":"Face Detection"}],"methods":[],"datasets_introduced":[{"slug":"fddb-360","name":"FDDB-360","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}