Multimodal Biometric Score Fusion Using Gaussian Mixture Model and Monte Carlo Method |
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Authors: | R Raghavendra Rao Ashok G Hemantha Kumar |
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Affiliation: | 1.Department of Studies in Computer Science,University of Mysore,Mysore,India;2.Department of E&C,Channabasaveshwara Institute of Technology,Gubbi-Tumkur,India |
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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. |
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