Papers › WIDER FACE: A Face Detection Benchmark

WIDER FACE: A Face Detection Benchmark

20 Nov 2015CVPR 2016 6arXiv:1511.06523archive 2025-07-28

Shuo Yang, Ping Luo, Chen Change Loy, Xiaoou Tang

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 dataset, 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. Dataset can be downloaded at: mmlab.ie.cuhk.edu.hk/projects/WIDERFace

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kabrau/FaceDetection mentioned on GitHubtf report

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Tasks

Face Detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Face Detection WIDER Face (Hard) Multiscale Cascade CNN AP 0.400 #37 of 40 Archive leaderboard report
Face Detection WIDER Face (Hard) Faceness-WIDER AP 0.315 #38 of 40 Archive leaderboard report
Face Detection WIDER Face (Hard) Two-stage CNN AP 0.304 #39 of 40 Archive leaderboard report
Face Detection WIDER Face (Medium) Multiscale Cascade CNN AP 0.636 #34 of 37 Archive leaderboard report
Face Detection WIDER Face (Medium) Faceness-WIDER AP 0.604 #35 of 37 Archive leaderboard report
Face Detection WIDER Face (Medium) Two-stage CNN AP 0.589 #36 of 37 Archive leaderboard report

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