cvpr19-face-anti-spoofing's Introduction

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

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

Recent Update

2019.3.29: Final code is not ready, will update soon.

2019.3.10: code upload for the origanizers to reproduce.

Dependencies

imgaug==0.2.6
scikit-image==0.14.0
scikit-learn==0.19.2
tqdm==4.23.4
torch==0.4.1
torchvision==0.2.1

Pretrained models

download [models]

Train single-modal Model

train model_A with color imgs， patch size 48：

CUDA_VISIBLE_DEVICES=0 python train_CyclicLR.py --model=model_A --image_mode=color --image_size=48

infer

CUDA_VISIBLE_DEVICES=0 python train_CyclicLR.py --mode=infer_test --model=model_A --image_mode=color --image_size=48

Train multi-modal fusion model

train model A fusion model with multi-modal imgs， patch size 48：

CUDA_VISIBLE_DEVICES=0 python train_Fusion_CyclicLR.py --model=model_A --image_size=48

infer

CUDA_VISIBLE_DEVICES=0 python train_Fusion_CyclicLR.py --mode=infer_test --model=model_A --image_size=48

For the origanizers to reproduce final two submissions

unzip the models.zip in the root folder and infer all the trained models

run ensemble script submission.py to generate the final two submissions in phase2: (test_first.txt and test_second.txt)

python submission.py

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}
}