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Adaptive Cascade Classifier based Multimodal Biometric Recognition and Identification System

Ujwalla Gawande, Kamal Hajari Published in Artificial Intelligence

International Journal of Applied Information Systems
Year of Publication: 2013
© 2012 by IJAIS Journal
10.5120/ijais13-451019
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  1. Ujwalla Gawande and Kamal Hajari. Article: Adaptive Cascade Classifier based Multimodal Biometric Recognition and Identification System. International Journal of Applied Information Systems 6(2):42-47, September 2013. BibTeX

    @article{key:article,
    	author = "Ujwalla Gawande and Kamal Hajari",
    	title = "Article: Adaptive Cascade Classifier based Multimodal Biometric Recognition and Identification System",
    	journal = "International Journal of Applied Information Systems",
    	year = 2013,
    	volume = 6,
    	number = 2,
    	pages = "42-47",
    	month = "September",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

Abstract

Biometrics consists of techniques for uniquely recognizing humans based upon one or more intrinsic physical or behavioral traits such as Iris, fingerprint, Face and Palm geometry etc. To overcome the limitations of Unimodal biometric system, a multimodal biometric is proposed. Amongst the various fusion levels, feature level fusion is expected to offer better recognition. Feature level fusion fused the extracted feature obtained from biometric traits. The proposed system is based on feature level fusion and adaptive cascade classifier for precise and reliable multimodal recognition and identification. Verification of Genuine and imposter individual classification is done using Backpropagation neural network. The simulation results demonstrated that a multibiometric template provides better recognition performance compared to a unibiometric template and adaptive cascade classification system significantly outperforms single classifier.

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Keywords

Neural network, Multimodal, single algorithmic, Multi algorithmic, Train and Test parameters and Backpropagation neural network.