SoftwareGift/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!!! 1.88ms(CPU)
| repo name | SoftwareGift/FeatherNets_Face-Anti-spoofing-Attack-Detection-Challenge-CVPR2019 |
| repo link | https://github.com/SoftwareGift/FeatherNets_Face-Anti-spoofing-Attack-Detection-Challenge-CVPR2019 |
| homepage | |
| language | Python |
| size (curr.) | 18321 kB |
| stars (curr.) | 580 |
| created | 2019-03-02 |
| license | Other |
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
- Send the your signed agreements to: Jun Wan, jun.wan@ia.ac.cn
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.
