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The Scientific World Journal
Volume 2015 (2015), Article ID 762341, 9 pages
http://dx.doi.org/10.1155/2015/762341
Research Article

Performance Evaluation of Multimodal Multifeature Authentication System Using KNN Classification

1Department of Electronics and Communication Engineering, Sri Venkateswara College of Engineering, Anna University, Sriperumbudur 602117, India
2Department of Electronics and Communication Engineering, Aditya Institute of Technology, Coimbatore 641107, India

Received 17 August 2015; Revised 19 October 2015; Accepted 25 October 2015

Academic Editor: Michele Nappi

Copyright © 2015 Gayathri Rajagopal and Ramamoorthy Palaniswamy. 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

This research proposes a multimodal multifeature biometric system for human recognition using two traits, that is, palmprint and iris. The purpose of this research is to analyse integration of multimodal and multifeature biometric system using feature level fusion to achieve better performance. The main aim of the proposed system is to increase the recognition accuracy using feature level fusion. The features at the feature level fusion are raw biometric data which contains rich information when compared to decision and matching score level fusion. Hence information fused at the feature level is expected to obtain improved recognition accuracy. However, information fused at feature level has the problem of curse in dimensionality; here PCA (principal component analysis) is used to diminish the dimensionality of the feature sets as they are high dimensional. The proposed multimodal results were compared with other multimodal and monomodal approaches. Out of these comparisons, the multimodal multifeature palmprint iris fusion offers significant improvements in the accuracy of the suggested multimodal biometric system. The proposed algorithm is tested using created virtual multimodal database using UPOL iris database and PolyU palmprint database.