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

The detail in our paper:FeatherNets: Convolutional Neural Networks as Light as Feather for Face Anti-spoofing

FeatherNetB Inference Time 1.87ms In CPU(i7,OpenVINO)

Params only 0.35M!! FLOPs 80M !!

In the first phase,we only use depth data for training ,and after ensemble ACER reduce to 0.0. But in the test phase, when we only use depth data, the best ACER is 0.0016.This result is not very satisfactory. If the security is not very high, just using single-mode data is a very good choice. In order to achieve better results, we use IR data to jointly predict the final result.

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 - - -

Our Pretrained Models(model checkpoints)

Link: Key:xzv8

decryption key: OTC-MMFD-11846496 Google Dirve

Recent Update

2019.4.4: updata data/

2019.3.10:code upload for the origanizers to reproduce.

2019.4.23:add our paper FeatherNets

2019.8.4: release our model checkpoint

2019.09.25: early mutilmodal method


install requeirements

conda env create -n env_name -f env.yml


CASIA-SURF Dataset[2]

How to download CASIA-SURF dataset?

1.Download, read the Contest Rules, and sign the agreement,link

  1. Send the your signed agreements to: Jun Wan,

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/ 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(trained on ImageNet2012)

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

download mobilenetv2 pretrained model from MobileNet V2 repo,or download from here,link: password: gi46 move them to ./checkpoints/pre-trainedModels/

1.train FishNet150

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

4Train MobileLiteNet54-SE

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

6Train FeatherNetB

python --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


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


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

Multimodal Methods

In the early days of the competition, I thought about some other multimodal methods. You can view the network structure here.( I have not been able to continue because of limited computing resources.


Code for 3rd Place Solution in Face Anti-spoofing Attack Detection Challenge @ CVPR2019,model only 0.35M!!! 1.88ms(CPU)




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