Product of Likelihood Ratio Scores Fusion of Face, Speech and Signature Based FJ-GMM for Biometrics Authentication Application Systems
Mathematics and Computer Science
Volume 2, Issue 5, September 2017, Pages: 51-65
Received: May 3, 2017; Accepted: Jul. 10, 2017; Published: Aug. 1, 2017
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Author
SOLTANE MOHAMED, Electrical Engineering & Computing Department, Faculty of Sciences & Technology, Doctor Yahia Fares University of Medea, Medea, Algeria
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Abstract
The paper proposes a likelihood ratio fusion of face, voice and signature multimodal biometrics verification application systems. Figueiredo-Jain (FJ) estimation algorithm of finite Gaussian mixture modal (GMM) is employed. Automated biometric systems for human identification measure a “signature” of the human body, compare the resulting characteristic to a database, and render an application dependent decision. These biometric systems for personal authentication and identification are based upon physiological or behavioral features which are typically distinctive, Multi-biometric systems, which consolidate information from multiple biometric sources, are gaining popularity because they are able to overcome limitations such as non-universality, noisy sensor data, large intra-user variations and susceptibility to spoof attacks that are commonly encountered in mono modal biometric systems. Simulation show that finite mixture modal (GMM) is quite effective in modelling the genuine and impostor score densities, fusion based the resulting density estimates achieves a significant performance on eNTERFACE 2005 multi-modal database based on face, signature and voice modalities.
Keywords
Gaussian Mixture Modal, Figueiredo-Jain, Biometrics Face Recognition, Speaker and Signature Verification Systems, Score Fusion, Likelihood Ratio
To cite this article
SOLTANE MOHAMED, Product of Likelihood Ratio Scores Fusion of Face, Speech and Signature Based FJ-GMM for Biometrics Authentication Application Systems, Mathematics and Computer Science. Vol. 2, No. 5, 2017, pp. 51-65. doi: 10.11648/j.mcs.20170205.11
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Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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