When Face Recognition Meets Occlusion: A New Benchmark (Webface-OCC)

By Baojin Huang, Zhongyuan Wang, Guangcheng Wang, Kui Jiang,Kangli Zeng, Zhen Han, Xin Tian, Yuhong Yang

Introduction

Webface-OCC is a simulated occlusion face recognition dataset, covering 804,704 face images of 10,575 subjects.

Webface-OCC+ is an upgraded occlusion face dataset by further encompassing challenging body occlusion types and annotated mask labels on the basis of our previous Webface-OCC.

Data & Pretrained Model Download

Request the download link from huangbaojin@whu.edu.cn (academic only).

Occlusion Face Recognition

Model Training

1. Install MXNet with GPU support.

pip install mxnet-cu100 # mxnet-cu102

2. Train occlusion face recognition models. In this part, we assume you are in the directory $ROOT/ArcFace_occ.

CUDA_VISIBLE_DEVICES='0,1' python -u train.py --network r50 --loss cosface --dataset emore

Citation

If you find Webface-OCC useful in your research, please consider to cite the following paper:

@inproceedings{huang2021when,
  title={When Face Recognition Meets Occlusion: A New Benchmark},
  author={Baojin Huang, Zhongyuan Wang, Guangcheng Wang, Kui Jiang, Kangli Zeng, Zhen Han, Xin Tian, Yuhong Yang},
  booktitle={ICASSP},
  year={2021}
}

Contact

huangbaojin@whu.edu.cn