PipeNet-PyTorch : RGB-D Face Anti-Spoofing Project

This is PyTorch code implementation for CVPR2020 workshop [paper] [arXiv] "PipeNet: Selective Modal Pipeline of Fusion Network for Multi-Modal Face Anti-Spoofing".

This approach won Global 3rd place in @CVPR2020 Chalearn Multi-modal Cross-ethnicity Face anti-spoofing Recognition Challenge (Multi_modal track).

Prerequisites

We use Anaconda3 with python > 3.6 , dependencies as below :

opencv-python 3.4.2

pytorch==1.2.0

imutils==0.5.3

scipy==1.2.1

numpy==1.18.1

tqdm==4.36.1

imgaug==0.2.6

Change CASIA-CeFA dataset ROOT PATH in code:

in line 5 of <-PROJECT ROOT->/data_helper.py file:

Replace <...> content in "DATA_ROOT = r'/<-root directory to your dataset->/CASIA-CeFA/'"

Dataset in Below structure:

+-- CASIA-CeFA

+-- phase1

    +-- train

    +-- dev

+-- phase1

    +-- test

<-PROJECT ROOT->/dataset/* contains splitted and shuffled file lists for train/val.
The tool for this work is under ./tools/train_filelist.ipynb

Train

python main.py --mode=train --dataset_name=4@1 
python main.py --mode=train --dataset_name=4@2 
python main.py --mode=train --dataset_name=4@3 

Test

python main.py --image_mode=fusion --mode=dev
python main.py --image_mode=fusion --mode=test

Citation

@inproceedings{pipenet,
  title={PipeNet: Selective Modal Pipeline of Fusion Network for Multi-Modal Face Anti-Spoofing},
  author={Yang, Qing and Zhu, Xia and Fwu, Jong-Kae and Ye, Yun and You, Ganmei and Zhu, Yuan},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  pages={644--645},
  year={2020}
}

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Any question, pls contact email: charles.q.yang@gmail.com or wechat: kim_young .