{"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/umdfaces-an-annotated-face-dataset-for","title":"UMDFaces: An Annotated Face Dataset for Training Deep Networks","arxiv_id":"1611.01484","date":"2016-11-04","proceeding":null,"authors":["Ankan Bansal","Anirudh Nanduri","Carlos Castillo","Rajeev Ranjan","Rama Chellappa"],"abstract":"Recent progress in face detection (including keypoint detection), and\nrecognition is mainly being driven by (i) deeper convolutional neural network\narchitectures, and (ii) larger datasets. However, most of the large datasets\nare maintained by private companies and are not publicly available. The\nacademic computer vision community needs larger and more varied datasets to\nmake further progress.\n  In this paper we introduce a new face dataset, called UMDFaces, which has\n367,888 annotated faces of 8,277 subjects. We also introduce a new face\nrecognition evaluation protocol which will help advance the state-of-the-art in\nthis area. We discuss how a large dataset can be collected and annotated using\nhuman annotators and deep networks. We provide human curated bounding boxes for\nfaces. We also provide estimated pose (roll, pitch and yaw), locations of\ntwenty-one key-points and gender information generated by a pre-trained neural\nnetwork. In addition, the quality of keypoint annotations has been verified by\nhumans for about 115,000 images. Finally, we compare the quality of the dataset\nwith other publicly available face datasets at similar scales.","url_abs":"http://arxiv.org/abs/1611.01484v2","url_pdf":"http://arxiv.org/pdf/1611.01484v2.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":"umdfaces-an-annotated-face-dataset-for","repo_url":"https://github.com/melgor/pyface","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"face-detection","task_name":"Face Detection"},{"task_slug":"face-recognition","task_name":"Face Recognition"},{"task_slug":"keypoint-detection","task_name":"Keypoint Detection"}],"methods":[],"datasets_introduced":[{"slug":"umdfaces","name":"UMDFaces","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1611.01484","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}