Advertisement

U-Finger: Multi-Scale Dilated Convolutional Network for Fingerprint Image Denoising and Inpainting

  • Ramakrishna PrabhuEmail author
  • Xiaojing Yu
  • Zhangyang Wang
  • Ding Liu
  • Anxiao (Andrew) Jiang
Conference paper
Part of the The Springer Series on Challenges in Machine Learning book series (SSCML)

Abstract

This paper studies the challenging problem of fingerprint image denoising and inpainting. To tackle the challenge of suppressing complicated artifacts (blur, brightness, contrast, elastic transformation, occlusion, scratch, resolution, rotation, and so on) while preserving fine textures, we develop a multi-scale convolutional network termed U-Finger. Based on the domain expertise, we show that the usage of dilated convolutions as well as the removal of padding have important positive impacts on the final restoration performance, in addition to multi-scale cascaded feature modules. Our model achieves the overall ranking of No.2 in the ECCV 2018 Chalearn LAP Inpainting Competition Track 3 (Fingerprint Denoising and Inpainting). Among all participating teams, we obtain the MSE of 0.0231 (rank 2), PSNR 16.9688 dB (rank 2), and SSIM 0.8093 (rank 3) on the hold-out testing set.

References

  1. 1.
    Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097–1105, 2012.Google Scholar
  2. 2.
    Dinh-Luan Nguyen, Kai Cao, and Anil K Jain. Robust minutiae extractor: Integrating deep networks and fingerprint domain knowledge. In 2018 International Conference on Biometrics (ICB). IEEE, 2018.Google Scholar
  3. 3.
    Yao Tang, Fei Gao, Jufu Feng, and Yuhang Liu. Fingernet: An unified deep network for fingerprint minutiae extraction. In Biometrics (IJCB), 2017 IEEE International Joint Conference on, pages 108–116. IEEE, 2017.Google Scholar
  4. 4.
    Kuldeep Singh, Rajiv Kapoor, and Raunaq Nayar. Fingerprint denoising using ridge orientation based clustered dictionaries. Neurocomputing, 167:418–423, 2015.CrossRefGoogle Scholar
  5. 5.
    Patrick Schuch, Simon Schulz, and Christoph Busch. Minutia-based enhancement of fingerprint samples. In Security Technology (ICCST), 2017 International Carnahan Conference on, pages 1–6. IEEE, 2017.Google Scholar
  6. 6.
    Mark Rahmes, Josef DeVaughn Allen, Abdelmoula Elharti, and Gnana Bhaskar Tenali. Fingerprint reconstruction method using partial differential equation and exemplar-based inpainting methods. In Biometrics Symposium, 2007, pages 1–6. IEEE, 2007.Google Scholar
  7. 7.
    Zhangyang Wang, Yingzhen Yang, Zhaowen Wang, Shiyu Chang, Jianchao Yang, and Thomas S Huang. Learning super-resolution jointly from external and internal examples. IEEE Transactions on Image Processing, 24(11):4359–4371, 2015.MathSciNetCrossRefGoogle Scholar
  8. 8.
    Ding Liu, Zhaowen Wang, Yuchen Fan, Xianming Liu, Zhangyang Wang, Shiyu Chang, and Thomas Huang. Robust video super-resolution with learned temporal dynamics. In Computer Vision (ICCV), 2017 IEEE International Conference on, pages 2526–2534. IEEE, 2017.Google Scholar
  9. 9.
    Zhangyang Wang, Shiyu Chang, Yingzhen Yang, Ding Liu, and Thomas S Huang. Studying very low resolution recognition using deep networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4792–4800, 2016.Google Scholar
  10. 10.
    Ding Liu, Bowen Cheng, Zhangyang Wang, Haichao Zhang, and Thomas S Huang. Enhance visual recognition under adverse conditions via deep networks. arXiv preprint arXiv:1712.07732, 2017.Google Scholar
  11. 11.
    Junyuan Xie, Linli Xu, and Enhong Chen. Image denoising and inpainting with deep neural networks. In Advances in neural information processing systems, pages 341–349, 2012.Google Scholar
  12. 12.
    Ding Liu, Bihan Wen, Xianming Liu, Zhangyang Wang, and Thomas S Huang. When image denoising meets high-level vision tasks: A deep learning approach. arXiv preprint arXiv:1706.04284, 2017.Google Scholar
  13. 13.
    Zhangyang Wang, Ding Liu, Shiyu Chang, Qing Ling, Yingzhen Yang, and Thomas S Huang. D3: Deep dual-domain based fast restoration of jpeg-compressed images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2764–2772, 2016.Google Scholar
  14. 14.
    Houqiang Li, Zhenbo Lu, Zhangyang Wang, Qing Ling, and Weiping Li. Detection of blotch and scratch in video based on video decomposition. IEEE Transactions on Circuits and Systems for Video Technology, 23(11):1887–1900, 2013.CrossRefGoogle Scholar
  15. 15.
    Fisher Yu and Vladlen Koltun. Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122, 2015.Google Scholar
  16. 16.
    Yu Liu, Guanlong Zhao, Boyuan Gong, Yang Li, Ritu Raj, Niraj Goel, Satya Kesav, Sandeep Gottimukkala, Zhangyang Wang, Wenqi Ren, et al. Improved techniques for learning to dehaze and beyond: A collective study. arXiv preprint arXiv:1807.00202, 2018.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ramakrishna Prabhu
    • 1
    Email author
  • Xiaojing Yu
    • 2
  • Zhangyang Wang
    • 2
  • Ding Liu
    • 3
  • Anxiao (Andrew) Jiang
    • 2
  1. 1.Department of Electrical and Computer EngineeringTexas A&M UniversityCollege StationUSA
  2. 2.Department of Computer ScienceTexas A&M UniversityCollege StationUSA
  3. 3.Computer Vision and DeeplearningUniversity of Illinois at Urbana-ChampaignChampaignUSA

Personalised recommendations