zli2014 / FeatherNets_Face-Anti-spoofing-Attack-Detection-Challenge-CVPR2019

Code for 3rd Place Solution in Face Anti-spoofing Attack Detection Challenge @ CVPR2019,model only 0.35M!!!

FeatherNets for Face Anti-spoofing Attack Detection Challenge@CVPR2019[1]

Params only 0.35M!! FLOPs 80M !!

Results on the validation set

model name	ACER	TPR@FPR=10E-2	TPR@FPR=10E-3	FP	FN	epoch	params	FLOPs
FishNet150	0.00144	0.999668	0.998330	19	0	27	24.96M	6452.72M
FishNet150	0.00181	1.0	0.9996	24	0	52	24.96M	6452.72M
FishNet150	0.00496	0.998664	0.990648	48	8	16	24.96M	6452.72M
MobileNet v2	0.00228	0.9996	0.9993	28	1	5	2.23M	306.17M
MobileNet v2	0.00387	0.999433	0.997662	49	1	6	2.23M	306.17M
MobileNet v2	0.00402	0.9996	0.992623	51	1	7	2.23M	306.17M
MobileLiteNet54	0.00242	1.0	0.99846	32	0	41	0.57M	270.91M
MobileLiteNet54-se	0.00242	1.0	0.996994	32	0	69	0.57M	270.91M
FeatherNetA	0.00261	1.00	0.961590	19	7	51	0.35M	79.99M
FeatherNetB	0.00168	1.0	0.997662	20	1	48	0.35M	83.05M
Ensembled all	0.0000	1.0	1.0	0	0	-	-	-

Prerequisites

install requeirements

conda env create -n env_name -f env.yml

Data

CASIA-SURF Dataset[2]

Our Private Dataset(Available Soon)

Data index tree

├── data
│   ├── our_realsense
│   ├── Training
│   ├── Val
│   ├── Testing

Download and unzip our private Dataset into the ./data directory. Then run data/fileList.py to prepare the file list.

Data Augmentation

Method	Settings
Random Flip	True
Random Crop	8% ~ 100%
Aspect Ratio	3/4 ~ 4/3
Random PCA Lighting	0.1

Train the model

Download pretrained models

download fishnet150 pretrained model from FishNet150 repo(Model trained without tricks )

download mobilenetv2 pretrained model from MobileNet V2 repo,or download from here,link: https://pan.baidu.com/s/11Hz50zlMyp3gtR9Bhws-Dg password: gi46 move them to ./checkpoints/pre-trainedModels/

1.train FishNet150

nohup python main.py --config="cfgs/fishnet150-32.yaml" --b 32 --lr 0.01 --every-decay 30 --fl-gamma 2 >> fishnet150-train.log &

2.train MobileNet V2

nohup python main.py --config="cfgs/mobilenetv2.yaml" --b 32 --lr 0.01 --every-decay 40 --fl-gamma 2 >> mobilenetv2-bs32-train.log &

Commands to train the model:

3Train MobileLiteNet54

python main.py --config="cfgs/MobileLiteNet54-32.yaml" --every-decay 60 -b 32 --lr 0.01 --fl-gamma 3 >>FNet54-bs32-train.log

4Train MobileLiteNet54-SE

python main.py --config="cfgs/MobileLiteNet54-se-64.yaml" --b 64 --lr 0.01  --every-decay 60 --fl-gamma 3 >> FNet54-se-bs64-train.log

5Train FeatherNetA

python main.py --config="cfgs/FeatherNetA-32.yaml" --b 32 --lr 0.01  --every-decay 60 --fl-gamma 3 >> MobileLiteNetA-bs32-train.log

6Train FeatherNetB

python main.py --config="cfgs/FeatherNetB-32.yaml" --b 32 --lr 0.01  --every-decay 60 --fl-gamma 3 >> MobileLiteNetB-bs32--train.log

How to create a submission file

example:

python main.py --config="cfgs/mobilenetv2.yaml" --resume ./checkpoints/mobilenetv2_bs32/_4_best.pth.tar --val True --val-save True

Ensemble

for validation

run EnsembledCode_val.ipynb

for test

run EnsembledCode_test.ipynb

notice:Choose a few models with large differences in prediction results

Serialized copy of the trained model

You can download my artifacts folder which I used to generate my final submissions: Available Soon

[1] ChaLearn Face Anti-spoofing Attack Detection Challenge@CVPR2019,link

[2] Shifeng Zhang, Xiaobo Wang, Ajian Liu, Chenxu Zhao, Jun Wan, Sergio Escalera, Hailin Shi, Zezheng Wang, Stan Z. Li, " CASIA-SURF: A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing ", arXiv, 2018 PDF