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Iris Matching by Local Extremum Points of Multiscale Taylor Expansion

  • Algirdas Bastys
  • Justas Kranauskas
  • Rokas Masiulis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)

Abstract

Random distribution of features in iris image texture allows to perform iris-based personal authentication with high confidence. We propose to use the most significant local extremum points of the first two Taylor expansion coefficients as descriptors of the iris texture. A measure of similarity that is robust to moderate inaccuracies in iris segmentation is presented for the proposed features. We provide experimental results of verification quality for four commonly used iris data-sets. Strong and weak aspects of the proposed approach are also discussed.

Keywords

Local Extremum Iris Image Equal Error Rate Iris Recognition Local Extremum Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Algirdas Bastys
    • 1
  • Justas Kranauskas
    • 1
  • Rokas Masiulis
    • 1
  1. 1.Department of Computer Science II, Faculty of Mathematics and InformaticsVilnius UniversityLithuania

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