{"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/multi-view-face-detection-using-deep","title":"Multi-view Face Detection Using Deep Convolutional Neural Networks","arxiv_id":"1502.02766","date":"2015-02-10","proceeding":null,"authors":["Sachin Sudhakar Farfade","Mohammad Saberian","Li-Jia Li"],"abstract":"In this paper we consider the problem of multi-view face detection. While\nthere has been significant research on this problem, current state-of-the-art\napproaches for this task require annotation of facial landmarks, e.g. TSM [25],\nor annotation of face poses [28, 22]. They also require training dozens of\nmodels to fully capture faces in all orientations, e.g. 22 models in HeadHunter\nmethod [22]. In this paper we propose Deep Dense Face Detector (DDFD), a method\nthat does not require pose/landmark annotation and is able to detect faces in a\nwide range of orientations using a single model based on deep convolutional\nneural networks. The proposed method has minimal complexity; unlike other\nrecent deep learning object detection methods [9], it does not require\nadditional components such as segmentation, bounding-box regression, or SVM\nclassifiers. Furthermore, we analyzed scores of the proposed face detector for\nfaces in different orientations and found that 1) the proposed method is able\nto detect faces from different angles and can handle occlusion to some extent,\n2) there seems to be a correlation between dis- tribution of positive examples\nin the training set and scores of the proposed face detector. The latter\nsuggests that the proposed methods performance can be further improved by using\nbetter sampling strategies and more sophisticated data augmentation techniques.\nEvaluations on popular face detection benchmark datasets show that our\nsingle-model face detector algorithm has similar or better performance compared\nto the previous methods, which are more complex and require annotations of\neither different poses or facial landmarks.","url_abs":"http://arxiv.org/abs/1502.02766v3","url_pdf":"http://arxiv.org/pdf/1502.02766v3.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":"multi-view-face-detection-using-deep","repo_url":"https://github.com/FlameRB/Face-Detection-using-Deep-Learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"multi-view-face-detection-using-deep","repo_url":"https://github.com/do4am/MultiviewFaceDetector","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"multi-view-face-detection-using-deep","repo_url":"https://github.com/PuchatekwSzortach/face_detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"face-detection","task_name":"Face Detection"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1502.02766","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}