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On Comparing Verification Performances of Multimodal Biometrics Fusion Techniques
| 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
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.
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