zllrunning / CVPR19-Face-Anti-spoofing

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

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

2019.3.22: 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

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

infer

CUDA_VISIBLE_DEVICES=0 python train_CyclicLR.py --mode=infer_test --model=model_A --image_mode=color --image_size=32
CUDA_VISIBLE_DEVICES=0 python train_CyclicLR.py --mode=infer_test --model=model_A --image_mode=depth --image_size=32
CUDA_VISIBLE_DEVICES=0 python train_CyclicLR.py --mode=infer_test --model=model_A --image_mode=ir    --image_size=32

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

CUDA_VISIBLE_DEVICES=0 python train_CyclicLR.py --mode=infer_test --model=model_A --image_mode=color --image_size=64
CUDA_VISIBLE_DEVICES=0 python train_CyclicLR.py --mode=infer_test --model=model_A --image_mode=depth --image_size=64
CUDA_VISIBLE_DEVICES=0 python train_CyclicLR.py --mode=infer_test --model=model_A --image_mode=ir    --image_size=64

CUDA_VISIBLE_DEVICES=0 python train_Fusion_CyclicLR.py --mode=infer_test --model=baseline --image_size=32
CUDA_VISIBLE_DEVICES=0 python train_Fusion_CyclicLR.py --mode=infer_test --model=baseline --image_size=48
CUDA_VISIBLE_DEVICES=0 python train_Fusion_CyclicLR.py --mode=infer_test --model=baseline --image_size=64

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

python submission.py