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Computational Intelligence and Neuroscience
Volume 2017, Article ID 9345969, 9 pages
https://doi.org/10.1155/2017/9345969
Research Article

Multimodal Personal Verification Using Likelihood Ratio for the Match Score Fusion

1Computer Science Department, University of Lac Hong, Dong Nai 810000, Vietnam
2Computer Science Department, VNUHCM-University of Science, Ho Chi Minh City 700000, Vietnam

Correspondence should be addressed to Thai Hoang Le; nv.ude.sumch.tif@iahthl

Received 3 May 2017; Revised 14 August 2017; Accepted 24 September 2017; Published 31 October 2017

Academic Editor: George A. Papakostas

Copyright © 2017 Long Binh Tran and Thai Hoang Le. 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

In this paper, the authors present a novel personal verification system based on the likelihood ratio test for fusion of match scores from multiple biometric matchers (face, fingerprint, hand shape, and palm print). In the proposed system, multimodal features are extracted by Zernike Moment (ZM). After matching, the match scores from multiple biometric matchers are fused based on the likelihood ratio test. A finite Gaussian mixture model (GMM) is used for estimating the genuine and impostor densities of match scores for personal verification. Our approach is also compared to some different famous approaches such as the support vector machine and the sum rule with min-max. The experimental results have confirmed that the proposed system can achieve excellent identification performance for its higher level in accuracy than different famous approaches and thus can be utilized for more application related to person verification.