Patch-based Methods for Face Anti-spoofing (FAS) & Code for CVPR2019 FAS Attack Detection Challenge

This is the source code for 2nd palce solution to the ChaLearn Face Anti-spoofing Attack Detection Challenge hosted by ChaLearn.

Recent Update

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.

Dependencies

• imgaug==0.4.0
• torch==1.9.0
• torchvision==0.10.0

Pretrained models

download [models.2021]

CASIA-SURF validation score (ACER)

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 single-modal Model

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 multi-modal fusion model

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

ViT for Multi-modal Face Anti-spoofing

CUDA_VISIBLE_DEVICES=0 python train_fusion.py --model=ViTFusion --image_size=96 --image_patch 16

Citation

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}
}

Contact

If you have any questions, feel free to E-mail me via: taoshen.seu@gmail.com