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On the Individuality of the Iris Biometric

  • Sungsoo Yoon
  • Seung-Seok Choi
  • Sung-Hyuk Cha
  • Yillbyung Lee
  • Charles C. Tappert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3656)

Abstract

Biometric authentication has been considered a model for quantitatively establishing the discriminative power of biometric data. The dichotomy model classifies two biometric samples as coming either from the same person or from two different people. This paper reviews features, distance measures, and classifiers used in iris authentication. For feature extraction we compare simple binary and multi-level 2D wavelet features. For distance measures we examine scalar distances such as Hamming and Euclidean, feature vector and histogram distances. Finally, for the classifiers we compare Bayes decision rule, nearest neighbor, artificial neural network, and support vector machines. Of the eleven different combinations tested, the best one uses multi-level 2D wavelet features, the histogram distance, and a support vector machine classifier.

Keywords

Biometric individuality Dichotomy model Histogram Iris authentication 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Sungsoo Yoon
    • 1
    • 2
  • Seung-Seok Choi
    • 1
  • Sung-Hyuk Cha
    • 1
  • Yillbyung Lee
    • 2
  • Charles C. Tappert
    • 1
  1. 1.Computer Science DepartmentPace UniversityPleasantvilleUSA
  2. 2.School of Engineering, Information and Industrial EngineeringYonsei UniversitySeoulKorea

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