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Fingerprint Presentation Attack Detection by Channel-wise Feature Denoising

This is an official pytorch implementation of 'Fingerprint Presentation Attack Detection by Channel-wise Feature Denoising', which is accepted by IEEE Transactions on Information Forensics and Security.

Requirements

  • python 3.6

  • pytorch 1.1.0

  • torchvision 0.3.0

  • numpy 1.19.5

  • pandas 0.25.3

  • scikit-image

Pre-processing

  • Dataset

    Download the LivDet 2017 datasets.

  • Data Label Generation

    Move to the $root and generate the label:

      python data_find.py --data_path dataPath

    dataPath is the path of data.

Usage

  • Move to the $root and run:

    python train.py --save savePath

    savePath is the filename to save model, which is in $root

Citation

Please cite our work if it's useful for your research.

@article{liu2022fingerprint,
  title={Fingerprint Presentation Attack Detection by Channel-Wise Feature Denoising},
  author={Liu, Feng and Kong, Zhe and Liu, Haozhe and Zhang, Wentian and Shen, Linlin},
  journal={IEEE Transactions on Information Forensics and Security},
  volume={17},
  pages={2963--2976},
  year={2022},
  publisher={IEEE}
}