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Machine Learning Based Separation of Overlapped Latent Fingerprints

  • Branka Stojanović
  • Oge Marques
  • Aleksandar Nešković
Chapter
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Abstract

This chapter describes a machine learning based approach for overlapped fingerprint separation. The algorithm works in a block-based fashion: after producing an initial estimation of the orientation fields present in the overlapped fingerprint image, it uses a neural network to separate the mixed orientation fields, which are then post-processed to correct remaining errors and enhanced using the global orientation field enhancement model. The proposed separation method has been successfully tested on two different datasets.

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Copyright information

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Branka Stojanović
    • 1
  • Oge Marques
    • 2
  • Aleksandar Nešković
    • 3
  1. 1.Vlatacom Research and Development Institute Ltd BelgradeBelgradeSerbia
  2. 2.College of Engineering and Computer ScienceFlorida Atlantic UniversityBoca RatonUSA
  3. 3.School of Electrical EngineeringUniversity of BelgradeBelgradeSerbia

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