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
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
python Generate_Depth_Image.py
Code: under MIT license.
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}
}
Thanks Yao Feng etc. for their PRNet.