wider_face

WIDER FACE

WIDER FACE: A Face Detection Benchmark

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.

Introduction

To use the WIDER Face dataset you need to download it and extract to the data/WIDERFace folder. Annotation in the VOC format can be found in this repo. You should move the annotation files from WIDER_train_annotations and WIDER_val_annotations folders to the Annotation folders inside the corresponding directories WIDER_train and WIDER_val. Also annotation lists val.txt and train.txt should be copied to data/WIDERFace from WIDER_train_annotations and WIDER_val_annotations. The directory should be like this:

mmdetection
├── mmdet
├── tools
├── configs
├── data
│   ├── WIDERFace
│   │   ├── WIDER_train
│   |   │   ├──0--Parade
│   |   │   ├── ...
│   |   │   ├── Annotations
│   │   ├── WIDER_val
│   |   │   ├──0--Parade
│   |   │   ├── ...
│   |   │   ├── Annotations
│   │   ├── val.txt
│   │   ├── train.txt

After that you can train the SSD300 on WIDER by launching training with the ssd300_wider_face.py config or create your own config based on the presented one.

Citation

@inproceedings{yang2016wider,
   Author = {Yang, Shuo and Luo, Ping and Loy, Chen Change and Tang, Xiaoou},
   Booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
   Title = {WIDER FACE: A Face Detection Benchmark},
   Year = {2016}
}