A parametric copula-based framework for hypothesis testing using heterogeneous data

We present a parametric framework for the joint processing of heterogeneous data, specifically for a binary classification problem. Processing such a data set is not straightforward as heterogeneous data may not be commensurate. In addition, the signals may also exhibit statistical dependence due to overlapping fields of view. We propose a copula-based solution to incorporate statistical dependence between disparate sources of information. The important problem of identifying the best copula for binary classification problems is also addressed. Computer simulation results are presented to demonstrate the feasibility of our approach. The method is also tested on real-data provided by the National Institute of Standards and Technology (NIST) for a multibiometric face recognition application. Finally, performance limits are derived to study the influence of statistical dependence on classification performance.