On Comparing Verification Performances of Multimodal Biometrics Fusion Techniques

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International Journal of Computer Applications
© 2011 by IJCA Journal
Volume 33 - Number 7
Year of Publication: 2011
Authors:
Romaissaa Mazouni
Abdellatif Rahmoun
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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