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
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Python 3.6 (numpy, skimage, scipy)
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TensorFlow >= 1.4
Optional:
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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.)
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opencv2 (for showing results)
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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.
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