This is the source code for 2nd palce solution to the ChaLearn Face Anti-spoofing Attack Detection Challenge hosted by ChaLearn.
2021.11.24: Add ViT for patch-based FAS
2021.10.12: Add VisionPermutator, MLPMixer and ConvMixer for patch-based FAS
2019.3.10: Code upload for the origanizers to reproduce.
- imgaug==0.4.0
- torch==1.9.0
- torchvision==0.10.0
download [models.2021]
| Single-modal Model | Color | Depth | ir |
|---|---|---|---|
| FaceBagNet | 0.0672 | 0.0036 | 0.1003 |
| ConvMixer | 0.0311 | 0.0025 | 0.1073 |
| MLPMixer | 0.0584 | 0.0010 | 0.2382 |
| VisionPermutator(ViP) | 0.0570 | 0.0304 | 0.2571 |
| VisonTransformer(ViT) | 0.0683 | 0.0036 | 0.2799 |
| Multi-modal Model | patch size 32 | patch size 48 | patch size 64 |
|---|---|---|---|
| FaceBagNetFusion | 0.0009 | 0.0006 | 0.0007 |
| ViTFusion | 0.0169 | 0.0778 | 0.0375 |
train FaceBagNet with color imgs， patch size 48：
CUDA_VISIBLE_DEVICES=0 python train.py --model=FaceBagNet --image_mode=color --image_size=48
infer
CUDA_VISIBLE_DEVICES=0 python train.py --mode=infer_test --model=FaceBagNet --image_mode=color --image_size=48
train FaceBagNet fusion model with multi-modal imgs， patch size 48：
CUDA_VISIBLE_DEVICES=0 python train_fusion.py --model=FaceBagNetFusion --image_size=48
infer
CUDA_VISIBLE_DEVICES=0 python train_fusion.py --mode=infer_test --model=FaceBagNet --image_size=48
CUDA_VISIBLE_DEVICES=0 python train_fusion.py --model=ViTFusion --image_size=96 --image_patch 16
If you find this work or code is helpful in your research, please cite:
@InProceedings{Shen_2019_CVPR_Workshops,
author = {Shen, Tao and Huang, Yuyu and Tong, Zhijun},
title = {FaceBagNet: Bag-Of-Local-Features Model for Multi-Modal Face Anti-Spoofing},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}
If you have any questions, feel free to E-mail me via: [email protected]
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