SSDG

The implementation of Single-Side Domain Generalization for Face Anti-Spoofing.

The motivation of the proposed SSDG method:

An overview of the proposed SSDG method:

Congifuration Environment

• python 3.6
• pytorch 0.4
• torchvision 0.2
• cuda 8.0

Pre-training

Dataset.

Download the OULU-NPU, CASIA-FASD, Idiap Replay-Attack, and MSU-MFSD datasets.

Data Pre-processing.

MTCNN algotithm is utilized for face detection and face alignment. All the detected faces are normlaize to 256\times\256\times\3, where only RGB channels are utilized for training.

To be specific, we process every frame of each video and then utilize the sample_frames function in the utils/utils.py to sample frames during training.

Put the processed frames in the path $root/data/dataset_name.

Data Label Generation.

Move to the $root/data_label and generate the data label list:

python generate_label.py

Training

Move to the folder $root/experiment/testing_scenarios/ and just run like this:

python train_ssdg_full.py

The file config.py contains all the hype-parameters used during training.

Testing

Run like this:

python dg_test.py

Citation

Please cite our paper if the code is helpful to your research.

@InProceedings{Jia_2020_CVPR_SSDG,
    author = {Yunpei Jia and Jie Zhang and Shiguang Shan and Xilin Chen},
    title = {Single-Side Domain Generalization for Face Anti-Spoofing},
    booktitle = {Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    year = {2020}
}