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Applied Computational Intelligence and Soft Computing
Volume 2012 (2012), Article ID 242401, 7 pages
http://dx.doi.org/10.1155/2012/242401
Research Article

Four Machine Learning Algorithms for Biometrics Fusion: A Comparative Study

Informatics and Telematics Institute, Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece

Received 29 October 2011; Accepted 12 January 2012

Academic Editor: Cheng-Jian Lin

Copyright © 2012 I. G. Damousis and S. Argyropoulos. 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

We examine the efficiency of four machine learning algorithms for the fusion of several biometrics modalities to create a multimodal biometrics security system. The algorithms examined are Gaussian Mixture Models (GMMs), Artificial Neural Networks (ANNs), Fuzzy Expert Systems (FESs), and Support Vector Machines (SVMs). The fusion of biometrics leads to security systems that exhibit higher recognition rates and lower false alarms compared to unimodal biometric security systems. Supervised learning was carried out using a number of patterns from a well-known benchmark biometrics database, and the validation/testing took place with patterns from the same database which were not included in the training dataset. The comparison of the algorithms reveals that the biometrics fusion system is superior to the original unimodal systems and also other fusion schemes found in the literature.