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- Publisher Website: 10.1109/CVPR.2016.596
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Conference Paper: WIDER FACE: A face detection benchmark
Title | WIDER FACE: A face detection benchmark |
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Authors | |
Issue Date | 2016 |
Citation | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, v. 2016-December, p. 5525-5533 How to Cite? |
Abstract | © 2016 IEEE. Face detection is one of the most studied topics in the computer vision community. Much of the progresses have been made by the availability of face detection benchmark datasets. We show that there is a gap between current face detection performance and the real world requirements. To facilitate future face detection research, we introduce the WIDER FACE dataset1, which is 10 times larger than existing datasets. The dataset contains rich annotations, including occlusions, poses, event categories, and face bounding boxes. Faces in the proposed dataset are extremely challenging due to large variations in scale, pose and occlusion, as shown in Fig. 1. Furthermore, we show that WIDER FACE dataset is an effective training source for face detection. We benchmark several representative detection systems, providing an overview of state-of-the-art performance and propose a solution to deal with large scale variation. Finally, we discuss common failure cases that worth to be further investigated. |
Persistent Identifier | http://hdl.handle.net/10722/273585 |
ISSN | 2023 SCImago Journal Rankings: 10.331 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Yang, Shuo | - |
dc.contributor.author | Luo, Ping | - |
dc.contributor.author | Loy, Chen Change | - |
dc.contributor.author | Tang, Xiaoou | - |
dc.date.accessioned | 2019-08-12T09:56:02Z | - |
dc.date.available | 2019-08-12T09:56:02Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, v. 2016-December, p. 5525-5533 | - |
dc.identifier.issn | 1063-6919 | - |
dc.identifier.uri | http://hdl.handle.net/10722/273585 | - |
dc.description.abstract | © 2016 IEEE. Face detection is one of the most studied topics in the computer vision community. Much of the progresses have been made by the availability of face detection benchmark datasets. We show that there is a gap between current face detection performance and the real world requirements. To facilitate future face detection research, we introduce the WIDER FACE dataset1, which is 10 times larger than existing datasets. The dataset contains rich annotations, including occlusions, poses, event categories, and face bounding boxes. Faces in the proposed dataset are extremely challenging due to large variations in scale, pose and occlusion, as shown in Fig. 1. Furthermore, we show that WIDER FACE dataset is an effective training source for face detection. We benchmark several representative detection systems, providing an overview of state-of-the-art performance and propose a solution to deal with large scale variation. Finally, we discuss common failure cases that worth to be further investigated. | - |
dc.language | eng | - |
dc.relation.ispartof | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition | - |
dc.title | WIDER FACE: A face detection benchmark | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/CVPR.2016.596 | - |
dc.identifier.scopus | eid_2-s2.0-85009881905 | - |
dc.identifier.volume | 2016-December | - |
dc.identifier.spage | 5525 | - |
dc.identifier.epage | 5533 | - |
dc.identifier.isi | WOS:000400012305064 | - |
dc.identifier.issnl | 1063-6919 | - |