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WIDER FACE: A Face Detection Benchmark

Open access
Yang, Shuo, Luo, Ping, Loy, Chen Change, Tang, Xiaoou·2016·2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Abstract

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.

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Metadata

Authors
Yang, S., Luo, P., Loy, C. C., & Tang, X.
Venue
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
IEEE
Published
2016-06
Pages
5525-5533
ISSN
1063-6919 (ISSN-L)
OpenAlex
W2963566548
Cited by
1,830
References
4
FWCI
45.81
Type
conference paper
Language
en
Open access
green

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CoChat. (2026). Citation check: WIDER FACE: A Face Detection Benchmark. https://app.cochat.ai/papers/10.1109/cvpr.2016.596
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Yang, S., Luo, P., Loy, C. C., & Tang, X. (2016). WIDER FACE: A Face Detection Benchmark. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2016.596

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