On the Individuality of the Iris Biometric

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 netw...

• Type: Conference
• Year: 2005
• Where: ICIAR
• Authors: Sungsoo Yoon, Seung-Seok Choi, Sung-Hyuk Cha, Yillbyung Lee, Charles C. Tappert
• Keywords: Distance Measures, ICIAR 2005, Iris Biometric Data, Support Vector Machine