README.md

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

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