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.Postprint (published version
