Semiparametric score level fusion: Gaussian copula approach

N. Susyanyo, C.A.J. Klaassen, Raymond N.J. Veldhuis, Lieuwe Jan Spreeuwers

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    Score level fusion is an appealing method for combining multi-algorithms, multi- representations, and multi-modality biometrics due to its simplicity. Often, scores are assumed to be independent, but even for dependent scores, accord- ing to the Neyman-Pearson lemma, the likelihood ratio is the optimal score level fusion if the underlying distributions are known. However, in reality, the dis- tributions have to be estimated. The common approaches are using parametric and nonparametric models. The disadvantage of the parametric method is that sometimes it is very dicult to choose the appropriate underlying distribution, while the nonparametric method is computationally expensive when the dimen- sionality increases. Therefore, it is natural to relax the distributional assumption and make the computation cheaper using a semiparametric approach. In this paper, we will discuss the semiparametric score level fusion using Gaussian copula. The theory how this method improves the recognition perfor- mance of the individual systems is presented and the performance using synthetic data will be shown. We also apply our fusion method to some public biomet- ric databases (NIST and XMVTS) and compare the thus obtained recognition performance with that of several common score level fusion rules such as sum, weighted sum, logistic regression, and Gaussian Mixture Model.
    Original languageUndefined
    Title of host publicationProceedings of the 36th WIC Symposium on Information Theory in the Benelux
    Place of PublicationBrussels
    PublisherUniversité Libre de Bruxelles
    Number of pages8
    ISBN (Print)978-2-8052-0277-3
    Publication statusPublished - 6 May 2015
    Event36th WIC Symposium on Information Theory in the Benelux 2015 - Brussels, Belgium
    Duration: 6 May 20157 May 2015
    Conference number: 36

    Publication series

    PublisherUniversité Libre de Bruxelles


    Workshop36th WIC Symposium on Information Theory in the Benelux 2015


    • EWI-26075
    • SCS-Safety
    • Biometric
    • IR-96056
    • Gaussian copula
    • METIS-312633
    • Fusion

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