PRNet-Depth-Generation

Introduction

A implementaion of depth generation based on PRNet, which was used in the paper Exploiting Temporal and Depth Information for Multi-frame Face Anti-spoofing

Prerequisite

• Python 3.6 (numpy, skimage, scipy)

• TensorFlow >= 1.4

  Optional:

• dlib (for detecting face. You do not have to install if you can provide bounding box information. Other face detectors are ok if you want.)

• opencv2 (for showing results)

• Download the PRN trained model at BaiduDrive or GoogleDrive, and put it into Data/net-data

Test

python Generate_Depth_Image.py

License

Code: under MIT license.

Citation

If you use this code, please consider citing:

@inProceedings{wang2018fastd,
  title     = {Exploiting Temporal and Depth Information for Multi-frame Face Anti-spoofing},
  author    = {Zezheng Wang, Chenxu Zhao, Yunxiao Qin, Qiusheng Zhou, Guojun Qi, Jun Wan, Zhen Lei},
  booktitle = {arXiv:1811.05118},
  year      = {2018}
}
@inProceedings{feng2018prn,
  title     = {Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network},
  author    = {Yao Feng, Fan Wu, Xiaohu Shao, Yanfeng Wang, Xi Zhou},
  booktitle = {ECCV},
  year      = {2018}
}

Acknowledgements

Thanks Yao Feng etc. for their PRNet.