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On Comparing Verification Performances of Multimodal Biometrics Fusion Techniques

by Romaissaa Mazouni, Abdellatif Rahmoun
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 33 - Number 7
Year of Publication: 2011
Authors: Romaissaa Mazouni, Abdellatif Rahmoun
10.5120/4033-5774

Romaissaa Mazouni, Abdellatif Rahmoun . On Comparing Verification Performances of Multimodal Biometrics Fusion Techniques. International Journal of Computer Applications. 33, 7 ( November 2011), 24-29. DOI=10.5120/4033-5774

@article{ 10.5120/4033-5774,
author = { Romaissaa Mazouni, Abdellatif Rahmoun },
title = { On Comparing Verification Performances of Multimodal Biometrics Fusion Techniques },
journal = { International Journal of Computer Applications },
issue_date = { November 2011 },
volume = { 33 },
number = { 7 },
month = { November },
year = { 2011 },
issn = { 0975-8887 },
pages = { 24-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume33/number7/4033-5774/ },
doi = { 10.5120/4033-5774 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:19:33.953244+05:30
%A Romaissaa Mazouni
%A Abdellatif Rahmoun
%T On Comparing Verification Performances of Multimodal Biometrics Fusion Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 33
%N 7
%P 24-29
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Fusion of matching scores of multiple biometric traits is becoming more and more popular and is a very promising approach to enhance the system's accuracy. This paper presents a comparative study of several advanced artificial intelligence techniques (e.g. Particle Swarm Optimization, Genetic Algorithm, Adaptive Neuro Fuzzy Systems, etc...) as to fuse matching scores in a multimodal biometric system. The fusion was performed under three data conditions: clean, varied and degraded. Some normalization techniques are also performed prior fusion so to enhance verification performance. Moreover; it is shown that regardless the type of biometric modality , when fusing scores genetic algorithms and Particle Swarm Optimization techniques outperform other well-known techniques in a multimodal biometric system verification/identification.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Adaptive Neuro Fuzzy Systems (ANFIS) Genetic Algorithm (GA) Support Vector Machine (SVM) Unconstrained Cohort Normalization (UCN)

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