Multimodal Biometric Score Fusion Using Gaussian Mixture Model and Monte Carlo Method

Raghavendra, R. and Ashok, Rao and Hemantha Kumar, G. (2010) Multimodal Biometric Score Fusion Using Gaussian Mixture Model and Monte Carlo Method. Journal of Computer Science and Technology, 25 (4). pp. 771-782. ISSN 1860-4749

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Official URL: https://doi.org/10.1007/s11390-010-9364-7

Abstract

Multimodal biometric fusion is gaining more attention among researchers in recent days. As multimodal biometric system consolidates the information from multiple biometric sources, the effective fusion of information obtained at score level is a challenging task. In this paper, we propose a framework for optimal fusion of match scores based on Gaussian Mixture Model (GMM) and Monte Carlo sampling based hypothesis testing. The proposed fusion approach has the ability to handle: 1) small size of match scores as is more commonly encountered in biometric fusion, and 2) arbitrary distribution of match scores which is more pronounced when discrete scores and multimodal features are present. The proposed fusion scheme is compared with well established schemes such as Likelihood Ratio (LR) method and weighted SUM rule. Extensive experiments carried out on five different multimodal biometric databases indicate that the proposed fusion scheme achieves higher performance as compared with other contemporary state of art fusion techniques.

Item Type: Article
Subjects: D Physical Science > Computer Science
Divisions: Department of > Computer Science
Depositing User: lpa manjunath user
Date Deposited: 09 Jul 2019 10:01
Last Modified: 09 Jul 2019 10:01
URI: http://eprints.uni-mysore.ac.in/id/eprint/5009

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