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.
| Published in | Mathematics and Computer Science (Volume 2, Issue 5) |
| DOI | 10.11648/j.mcs.20170205.11 |
| Page(s) | 51-65 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2017. Published by Science Publishing Group |
Gaussian Mixture Modal, Figueiredo-Jain, Biometrics Face Recognition, Speaker and Signature Verification Systems, Score Fusion, Likelihood Ratio
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APA Style
SOLTANE MOHAMED. (2017). Product of Likelihood Ratio Scores Fusion of Face, Speech and Signature Based FJ-GMM for Biometrics Authentication Application Systems. Mathematics and Computer Science, 2(5), 51-65. https://doi.org/10.11648/j.mcs.20170205.11
ACS Style
SOLTANE MOHAMED. Product of Likelihood Ratio Scores Fusion of Face, Speech and Signature Based FJ-GMM for Biometrics Authentication Application Systems. Math. Comput. Sci. 2017, 2(5), 51-65. doi: 10.11648/j.mcs.20170205.11
AMA Style
SOLTANE MOHAMED. Product of Likelihood Ratio Scores Fusion of Face, Speech and Signature Based FJ-GMM for Biometrics Authentication Application Systems. Math Comput Sci. 2017;2(5):51-65. doi: 10.11648/j.mcs.20170205.11
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Download@article{10.11648/j.mcs.20170205.11,
author = {SOLTANE MOHAMED},
title = {Product of Likelihood Ratio Scores Fusion of Face, Speech and Signature Based FJ-GMM for Biometrics Authentication Application Systems},
journal = {Mathematics and Computer Science},
volume = {2},
number = {5},
pages = {51-65},
doi = {10.11648/j.mcs.20170205.11},
url = {https://doi.org/10.11648/j.mcs.20170205.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20170205.11},
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.},
year = {2017}
}
TY - JOUR T1 - Product of Likelihood Ratio Scores Fusion of Face, Speech and Signature Based FJ-GMM for Biometrics Authentication Application Systems AU - SOLTANE MOHAMED Y1 - 2017/08/01 PY - 2017 N1 - https://doi.org/10.11648/j.mcs.20170205.11 DO - 10.11648/j.mcs.20170205.11 T2 - Mathematics and Computer Science JF - Mathematics and Computer Science JO - Mathematics and Computer Science SP - 51 EP - 65 PB - Science Publishing Group SN - 2575-6028 UR - https://doi.org/10.11648/j.mcs.20170205.11 AB - 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. VL - 2 IS - 5 ER -
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