WIDER FACE: A Face Detection Benchmark

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 fut...

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Bibliographic Details
Published in2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 5525 - 5533
Main Authors Yang, Shuo, Luo, Ping, Loy, Chen Change, Tang, Xiaoou
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2016
Subjects
Benchmark testing
Face
Face detection
Histograms
Image color analysis
Proposals
Training
Online AccessGet full text
ISSN1063-6919
DOI10.1109/CVPR.2016.596

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Summary: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.
ISSN:1063-6919
DOI:10.1109/CVPR.2016.596