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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. image

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.


  • 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 --model=FaceBagNet --image_mode=color --image_size=48


CUDA_VISIBLE_DEVICES=0 python --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 --model=FaceBagNetFusion --image_size=48


CUDA_VISIBLE_DEVICES=0 python --mode=infer_test --model=FaceBagNet --image_size=48

ViT for Multi-modal Face Anti-spoofing

CUDA_VISIBLE_DEVICES=0 python --model=ViTFusion --image_size=96 --image_patch 16


If you find this work or code is helpful in your research, please cite:

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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