In multimodal fusion systems a normalization of the features or the scores is needed before the fusion process. In this work, in addition to the conventional methods, histogram equalization, which was recently introduced by the authors in multimodal systems, and Bi-Gaussian equalization, which takes into account the separate statistics of the genuine and impostor scores, and is introduced in this paper, are applied upon the scores in a multimodal SVM-based person verification system composed by prosodic, speech spectrum, and face information. Bi-Gaussian equalization has obtained the best results and outperform in more than a 23.25% the results obtained by Min-Max normalization.