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On Comparing Verification Performances of Multimodal Biometrics Fusion Techniques

Author(s): Romaissaa Mazouni | Abdellatif Rahmoun

Journal: International Journal of Computer Applications
ISSN 0975-8887

Volume: 33;
Issue: 7;
Start page: 24;
Date: 2011;
Original page

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

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 wellknown techniques in a multimodal biometric system verification/identification.
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