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Proceedings Paper

An artificial neural network based matching metric for iris identification
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Paper Abstract

The iris is currently believed to be the most accurate biometric for human identification. The majority of fielded iris identification systems are based on the highly accurate wavelet-based Daugman algorithm. Another promising recognition algorithm by Ives et al uses Directional Energy features to create the iris template. Both algorithms use Hamming distance to compare a new template to a stored database. Hamming distance is an extremely fast computation, but weights all regions of the iris equally. Work from multiple authors has shown that different regions of the iris contain varying levels of discriminatory information. This research evaluates four post-processing similarity metrics for accuracy impacts on the Directional Energy and wavelets based algorithms. Each metric builds on the Hamming distance method in an attempt to use the template information in a more salient manner. A similarity metric extracted from the output stage of a feed-forward multi-layer perceptron artificial neural network demonstrated the most promise. Accuracy tables and ROC curves of tests performed on the publicly available Chinese Academy of Sciences Institute of Automation database show that the neural network based distance achieves greater accuracy than Hamming distance at every operating point, while adding less than one percent computational overhead.

Paper Details

Date Published: 3 March 2008
PDF: 11 pages
Proc. SPIE 6812, Image Processing: Algorithms and Systems VI, 68120S (3 March 2008); doi: 10.1117/12.766725
Show Author Affiliations
Randy P. Broussard, U.S. Naval Academy (United States)
Lauren R. Kennell, U.S. Naval Academy (United States)
Robert W. Ives, U.S. Naval Academy (United States)
Ryan N. Rakvic, U.S. Naval Academy (United States)

Published in SPIE Proceedings Vol. 6812:
Image Processing: Algorithms and Systems VI
Jaakko T. Astola; Karen O. Egiazarian; Edward R. Dougherty, Editor(s)

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