On Comparing Verification Performances of Multimodal Biometrics Fusion Techniques
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10.5120/4033-5774 |
Romaissaa Mazouni and Abdellatif Rahmoun. Article: On Comparing Verification Performances of Multimodal Biometrics Fusion Techniques. International Journal of Computer Applications 33(7):24-29, November 2011. Full text available. BibTeX
@article{key:article,
author = {Romaissaa Mazouni and Abdellatif Rahmoun},
title = {Article: On Comparing Verification Performances of Multimodal Biometrics Fusion Techniques},
journal = {International Journal of Computer Applications},
year = {2011},
volume = {33},
number = {7},
pages = {24-29},
month = {November},
note = {Full text available}
}
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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