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

该研究介绍了 WIDER FACE 数据集，比现有数据集大 10 倍。数据集包含繁多的注释，如遮挡、姿势、事件类别和人脸边界框等，因此对人脸检测领域具有极高挑战性。通过对多个代表性检测系统进行基准测试，提出了识别大尺度变差的解决方案，并讨论了常见的失败案例以及未来方向。