/Fingerprint-2DPose-Dense-Voting

Pytorch implementation of paper "Estimating Fingerprint Pose via Dense Voting"

Primary LanguagePythonMIT LicenseMIT

Fingerprint 2D pose estimation

Pytorch implementation of paper "Estimating Fingerprint Pose via Dense Voting"

Requirements

  • numpy, scipy, imageio, PyYaml, Pillow
  • opencv==3.4.2.17
  • pytorch==1.10.0

Deploy

  1. download the trained model and unzip it
  2. place the model in the folder ./logs
  3. adjust the related parameters prefix in the file ./deploy_gridnet.py according to your image location
  4. run python deploy_gridnet.py -i <img_name.img_format>

Contact

If you have any questions about our work, please contact dyj17@mails.tsinghua.edu.cn

Citation

If you find this project helpful, please cite our paper:

@ARTICLE{10100723,
  author={Duan, Yongjie and Feng, Jianjiang and Lu, Jiwen and Zhou, Jie},
  journal={IEEE Transactions on Information Forensics and Security}, 
  title={Estimating Fingerprint Pose via Dense Voting}, 
  year={2023},
  volume={18},
  number={},
  pages={2493-2507},
  doi={10.1109/TIFS.2023.3266625}}