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)


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.


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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

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