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SeuTao
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Description

Code for 2nd Place Solution in Face Anti-spoofing Attack Detection Challenge @ CVPR2019

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Code for ChaLearn Face Anti-spoofing Attack Detection Challenge @ CVPR2019

This is the source code for my solution to the ChaLearn Face Anti-spoofing Attack Detection Challenge hosted by ChaLearn. image

Recent Update

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]

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=FaceBagNet --image_size=48
infer
CUDA_VISIBLE_DEVICES=0 python train_Fusion.py --mode=infer_test --model=FaceBagNet --image_size=48

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:

[email protected]

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