Biometrics refers to the automatic identification of an individual based on his/her physiological or behavioral traits. Unimodal biometric systems perform person recognition based on a single source of biometric information and are affected by problems like noisy sensor data, non-universality and lack of individuality of the chosen biometric trait. Some of the limitations imposed by unimodal biometric systems (that is, biometric systems that rely on the evidence of a single biometric trait) can be overcome by using multiple biometric modalities. Such systems, known as Multimodal biometric systems, are expected to be more reliable due to the presence of multiple, fairly independent pieces of evidence. A multimodal biometric system integrates information from multiple biometric sources to compensate for the limitations in per¬formance of each individual biometric system. An optimal framework for combining the matching scores from multiple modalities using the likelihood ratio statistic computed using the generalized den¬sities estimated from the genuine and impostor matching scores is being proposed in this paper. The motivation for using generalized densities is that some parts of the score distributions can be discrete in nature; thus, estimating the distribution using continuous densities may be inappropriate. The two ap¬proaches for combining evidence based on generalized densities: (i) the product rule, which assumes independence between the individual modal¬ities, and (ii) copula models, which consider the dependence between the matching scores of multiple modalities are being presented in this paper.