English

Abstract

Most Support Vector Machine (SVM) based systems make use of conventional methods for the normalization of the features or the scores previously to the fusion stage. In this work, in addition to the conventional methods, two equalization methods, histogram equalization, which was recently introduced in multimodal systems, and Bi-Gaussian equalization, which is presented in this paper, are applied upon the scores in a multimodal person verification system composed by prosodic, speech spectrum, and face information. The equalization techniques have obtained the best results; concretely, Bi-Gaussian equalization outperforms in more than a 22.19 % the results obtained by Min-Max normalization, the most used normalization technique in SVM fusion systems. The prosodic and speech spectrum scores have been provided by speech experts using records of the Switchboard I database and the face scores have been obtained by a face recognition system upon XM2VTS database.