Journal of the Korean Society for Industrial and Applied Mathematics

Korean Society for Industrial and Applied Mathematics (한국산업응용수학회)
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FINGERPRINT IMAGE DENOISING AND INPAINTING USING CONVOLUTIONAL NEURAL NETWORK

  • BAE, JUNGYOON (DEPARTMENT OF COMPUTATIONAL SCIENCE AND TECHNOLOGY, SEOUL NATIONAL UNIVERSITY) ;
  • CHOI, HAN-SOO (DEPARTMENT OF MATHEMATICAL SCIENCES / RESEARCH INSTITUTE OF MATHEMATICS, SEOUL NATIONAL UNIVERSITY) ;
  • KIM, SUJIN (DEPARTMENT OF COMPUTATIONAL SCIENCE AND TECHNOLOGY, SEOUL NATIONAL UNIVERSITY) ;
  • KANG, MYUNGJOO (DEPARTMENT OF MATHEMATICAL SCIENCES, SEOUL NATIONAL UNIVERSITY)
  • Received : 2020.08.25
  • Accepted : 2020.12.17
  • Published : 2020.12.25
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Abstract

Fingerprint authentication identifies a user based on the individual's unique fingerprint features. Fingerprint authentication methods are used in various real-life devices because they are convenient and safe and there is no risk of leakage, loss, or oblivion. However, fingerprint authentication methods are often ineffective when there is contamination of the given image through wet, dirty, dry, or wounded fingers. In this paper, a method is proposed to remove noise from fingerprint images using a convolutional neural network. The proposed model was verified using the dataset from the ChaLearn LAP Inpainting Competition Track 3-Fingerprint Denoising and Inpainting, ECCV 2018. It was demonstrated that the model proposed in this paper obtains better results with respect to the methods that achieved high performances in the competition.

Keywords

Acknowledgement

Myungjoo Kang was supported by the National Research Foundation of Korea (2015R1A5A1009350) and the ICT R&D program of MSIT/IITP(No. 1711117093)

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