Abstract

Face detection is one of the most studied topics in thecomputer vision community. Much of the progresses havebeen made by the availability of face detection benchmarkdatasets. We show that there is a gap between current facedetection performance and the real world requirements. Tofacilitate future face detection research, we introduce theWIDER FACE dataset1 , which is 10 times larger than exist-ing datasets. The dataset contains rich annotations, includ-ing occlusions, poses, event categories, and face boundingboxes. Faces in the proposed dataset are extremely chal-lenging due to large variations in scale, pose and occlusion,as shown in Fig. 1. Furthermore, we show that WIDERFACE dataset is an effective training source for face de-tection. We benchmark several representative detection sys-tems, providing an overview of state-of-the-art performanceand propose a solution to deal with large scale variation.Finally, we discuss common failure cases that worth to befurther investigated.