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Proceedings Paper

Biorthogonal-wavelets-based iris recognition
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Paper Abstract

Iris recognition has been demonstrated to be an efficient technology for doing personal identification. In this work, a method to perform iris recognition using biorthogonal wavelets is introduced. Effective use of biorthogonal wavelets using a lifting technique to encode the iris information is demonstrated. This new method minimizes built in noise of iris images using in-band thresholding in order to provide better mapping and encoding of the relevant information. Comparison of Gabor encoding, similar to the method used by Daugman and others, and biorthogonal wavelet encoding is performed. While Daugman's approach is a well-proven algorithm, the effectiveness of our algorithm is shown for the CASIA database, based on the ability to classify inter and intra class distributions, and may provide more flexibility for non-ideal images. The method was tested on the CASIA dataset of iris images with over 4,536 intra-class and 566,244 inter-class comparisons made. After calculating Hamming distances and for the selected threshold value of 0.4, FRR and FAR values were 13.6% and 0.6% using Gabor filter and 0% and 0.03% using the biorthogonal wavelets.

Paper Details

Date Published: 28 March 2005
PDF: 9 pages
Proc. SPIE 5779, Biometric Technology for Human Identification II, (28 March 2005); doi: 10.1117/12.604212
Show Author Affiliations
Aditya Abhyankar, Clarkson Univ. (United States)
Lawrence A. Hornak, West Virginia Univ. (United States)
Stephanie Schuckers, Clarkson Univ. (United States)
West Virginia Univ. (United States)

Published in SPIE Proceedings Vol. 5779:
Biometric Technology for Human Identification II
Anil K. Jain; Nalini K. Ratha, Editor(s)

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