CompNet

CompNet: Competitive Neural Network for Palmprint Recognition Using Learnable Gabor Kernels

[ paper | supp | cite | license ]

1. Related Materials

• Paper: online

• Supplementary Material: pdf

• Pretrained Models: google or baidu

• Publicly Available Datasets: Tongji, IITD, REST, NTU, XJTU-UP

2. Visualizations

Fig. 2.1 CB17x17_Gabor feature maps obtained at different epochs

Fig. 2.2 CB17x17_SCC feature maps obtained at different epochs

Fig. 2.3 CB7x7_Conv1 feature maps obtained at different epochs

Fig. 2.4 CB17x17_Conv2 feature maps obtained at different epochs

Each row represents feature maps obtained from one ROI image, and each column corresponds to a single feature channel.

3. Requirements

Recommanded hardware requirement for training:

• GPU Mem $\gt$ 6G
• CPU Mem $\geq$ 16G (32G is recommended for highspeed data augmentation)

Software development environment:

• cuda&cudnn&gpu-driver
• Anaconda: download & install
• PyTorch: installation command lines are as follows

  conda create -n compnet python=3.8 
  conda activate compnet
  
  conda install pytorch==1.7.0 torchvision==0.8.0 torchaudio==0.7.0 cudatoolkit=11.0 -c pytorch
  
  pip install -r requirements.txt
  

tips:

• requirements.txt could be found at the root folder of this project
• for PyTorch 1.7 with other CUDA versions, please refer to the official pytorch installation commands
• the speed of different network servers and conda sources varies a lot when install the above packages
• for more details of the software environment, pls refer to the pip list rst
• version selection strategy: pytorch->cuda->cudnn

Other tested versions:

• cuda: 10.2 / 11.0
• cudnn: 7.6.5.32 / 9.1.0.70
• pytorch: 1.2 / 1.7
• torchvision: 0.4 / 0.8
• python: 3.7.4 / 3.8.19
• opencv: 3.2.7 / 4.8.1.78
• numpy: 1.16.4 / 1.24.3
• scikit-learn: 0.21.3 / 1.0.2
• scipy: 1.3.1 / 1.10.1

4. PyTorch Implementation

Configurations

1. modify path1 and path2 in genText.py

   • path1: path of the training set (e.g., Tongji session1)
   • path2: path of the testing set (e.g., Tongji session2)

2. modify num_classes in train.py, test.py, and inference.py

   • Tongji: 600, IITD: 460, REST: 358, XJTU-UP: 200, KTU: 145

Commands

cd path/to/CompNet/
#in the CompNet folder:

#prepare data
cp ./data/for_reference/genText_xxx.py ./data/genText.py
#where xxx is the dataset name, e.g., tongji =>genText_tongji.py
Modify the DB path variable in ./data/genText.py
#the sample naming format should be consistent with the script's requirements

#generate the training and testing data sets
python ./data/genText.py
mv ./train.txt ./data/
mv ./test.txt ./data/

#train the network
python train.py
#test the model
python test.py
#inference
python inference.py

#Metrics
#obtain the genuine-impostor matching score distribution curve
python    getGI.py   ./rst/veriEER/scores_xxx.txt    scores_xxx
#obtain the EER and the ROC curve
python    getEER.py   ./rst/veriEER/scores_xxx.txt    scores_xxx

The .pth file will be generated at the current folder, and all the other results will be generated in the ./rst folder.

🗞️tips: GPU -> CPU (more details):

# training on GPU, test on CPU
torch.load('net_params.pth', map_location='cpu')

5. Framework

compnet(
  (cb1): CompetitiveBlock(
    (gabor_conv2d): GaborConv2d()
    (argmax): Softmax(dim=1)
    (conv1): Conv2d(9, 32, kernel_size=(5, 5), stride=(1, 1))
    (maxpool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (conv2): Conv2d(32, 12, kernel_size=(1, 1), stride=(1, 1))
  )
  (cb2): CompetitiveBlock(
    (gabor_conv2d): GaborConv2d()
    (argmax): Softmax(dim=1)
    (conv1): Conv2d(9, 32, kernel_size=(5, 5), stride=(1, 1))
    (maxpool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (conv2): Conv2d(32, 12, kernel_size=(1, 1), stride=(1, 1))
  )
  (cb3): CompetitiveBlock(
    (gabor_conv2d): GaborConv2d()
    (argmax): Softmax(dim=1)
    (conv1): Conv2d(9, 32, kernel_size=(5, 5), stride=(1, 1))
    (maxpool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (conv2): Conv2d(32, 12, kernel_size=(1, 1), stride=(1, 1))
  )
  (fc): Linear(in_features=9708, out_features=512, bias=True)
  (drop): Dropout(p=0.25, inplace=False)
  (arclayer): ArcMarginProduct()
)

6. Citation

🌻If it helps you, we would like you to cite the following paper:🌱

@article{spl2021compnet,
author={Liang, Xu and Yang, Jinyang and Lu, Guangming and Zhang, David},
journal={IEEE Signal Processing Letters},
title={CompNet: Competitive Neural Network for Palmprint Recognition Using Learnable Gabor Kernels},
year={2021},
volume={28},
number={},
pages={1739-1743},
doi={10.1109/LSP.2021.3103475}}

Xu Liang, Jinyang Yang, Guangming Lu and David Zhang, "CompNet: Competitive Neural Network for Palmprint Recognition Using Learnable Gabor Kernels," in IEEE Signal Processing Letters, vol. 28, pp. 1739-1743, 2021, doi: 10.1109/LSP.2021.3103475.

License

This project is under the CC-BY-NC 4.0 license. See LICENSE for details.

Acknowledgement

Portions of the research use the REST'2016 Database collected by the Research Groups in Intelligent Machines, University of Sfax, Tunisia. We would also like to thank the organizers (IITD, Tongji, REgim, XJTU, and NTU) for allowing us to use their datasets.

References

[1] J. Deng, J. Guo, N. Xue and S. Zafeiriou, “ArcFace: Additive angularmargin loss for deep face recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4690–4699, Jun. 2019.

[2] Arcface PyTorch Implementation (ArcMarginProduct). [Online]. Available: arcface

[3] P. Chen, W. Li, L. Sun, X. Ning, L. Yu, and L. Zhang, “LGCN: LearnableGabor convolution network for human gender recognition in the wild,” IEICE Transactions on Information and Systems, 102(10), pp. 2067–2071, Oct. 2019.

[4] A. Genovese, V. Piuri, K. N. Plataniotis and F. Scotti, “PalmNet: Gabor-PCA convolutional networks for touchless palmprint recognition,” IEEE Transactions on Information Forensics and Security, 14(12), pp. 3160–3174, Dec. 2019. palmnet

[5] X. Liang, D. Fan, J. Yang, W. Jia, G. Lu and D. Zhang, "PKLNet: Keypoint Localization Neural Network for Touchless Palmprint Recognition Based on Edge-Aware Regression," in IEEE Journal of Selected Topics in Signal Processing, 17(3), pp. 662-676, May 2023, doi: 10.1109/JSTSP.2023.3241540. (Palmprint ROI extraction) pklnet🖖

[6] X. Liang, Z. Li, D. Fan, B. Zhang, G. Lu and D. Zhang, "Innovative Contactless Palmprint Recognition System Based on Dual-Camera Alignment," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 10, pp. 6464-6476, Oct. 2022, doi: 10.1109/TSMC.2022.3146777. (Bimodal alignment) ppnet🖐️

[7] PyTorch API Documents: https://pytorch.org/docs/stable/index.html