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On the Individuality of the Iris Biometric
| Citations: | 12 - 5 self |
BibTeX
@MISC{Yoon_onthe,
author = {Sungsoo Yoon and Seung-seok Choi and Sung-hyuk Cha and Yillbyung Lee},
title = {On the Individuality of the Iris Biometric},
year = {}
}
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Abstract
We consider quantitatively establishing the discriminative power of iris biometric data. It is difficult, however, to establish that any biometric modality is capable of distinguishing every person because the classification task has an extremely large and unspecified number of classes. Here, we propose a methodology to establish a measure of discrimination that is statistically inferable. To establish the inherent distinctness of the classes, i.e., to validate individuality, we transform the many class problem into a dichotomy by using a distance measure between two samples of the same class and between those of two different classes. Various features, distance measures, and classifiers are evaluated. For feature extraction we compare simple binary and multilevel 2D wavelet features. For distance measures we examine scalar distances, feature vector distances, 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 his-togram distance, and a support vector machine classifier. Keywords: Biometric individuality, Dichotomy, Iris. 1.
Keyphrases
distance measure iris biometric wavelet feature feature extraction histogram distance eleven different combination various feature feature vector distance iris biometric data biometric individuality classification task artificial neural network discriminative power many class problem his-togram distance scalar distance support vector machine support vector machine classifier biometric modality different class inherent distinctness unspecified number bayes decision rule
