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Advances in Artificial Intelligence
Volume 2012 (2012), Article ID 124176, 9 pages
http://dx.doi.org/10.1155/2012/124176
Research Article

Chaotic Neural Network for Biometric Pattern Recognition

Department of Computer Science, University of Calgary, Calgary, AB, Canada T2N 1N4

Received 8 December 2011; Revised 24 February 2012; Accepted 25 February 2012

Academic Editor: Sheryl Brahnam

Copyright © 2012 Kushan Ahmadian and Marina Gavrilova. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Biometric pattern recognition emerged as one of the predominant research directions in modern security systems. It plays a crucial role in authentication of both real-world and virtual reality entities to allow system to make an informed decision on granting access privileges or providing specialized services. The major issues tackled by the researchers are arising from the ever-growing demands on precision and performance of security systems and at the same time increasing complexity of data and/or behavioral patterns to be recognized. In this paper, we propose to deal with both issues by introducing the new approach to biometric pattern recognition, based on chaotic neural network (CNN). The proposed method allows learning the complex data patterns easily while concentrating on the most important for correct authentication features and employs a unique method to train different classifiers based on each feature set. The aggregation result depicts the final decision over the recognized identity. In order to train accurate set of classifiers, the subspace clustering method has been used to overcome the problem of high dimensionality of the feature space. The experimental results show the superior performance of the proposed method.