Semiparametric score level fusion: Gaussian copula approach

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.