WIDER FACE: A Face Detection Benchmark

TL;DR

The paper introduces the WIDER FACE dataset, a large and challenging benchmark for face detection with diverse annotations, aiming to advance research and address real-world detection challenges.

Contribution

It presents a new, extensive face detection dataset with rich annotations and demonstrates its effectiveness for training and benchmarking detection systems.

Findings

WIDER FACE dataset is 10 times larger than previous datasets.

Faces in the dataset exhibit high variability in scale, pose, and occlusion.

Benchmark results highlight current detection challenges and guide future improvements.

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 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