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:https://pan.baidu.com/s/1vlKePiWYFYNxefD9Ld16cQ Key:xzv8

decryption key: OTC-MMFD-11846496 Google Dirve

Recent Update

2019.4.4: updata data/fileList.py

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

Prerequisites

install requeirements

conda env create -n env_name -f env.yml

Data

CASIA-SURF Dataset[2]

How to download CASIA-SURF dataset?

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

2. Send the your signed agreements to: Jun Wan, [email protected]

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(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: 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

Multimodal Methods

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