Histogram Equalization in SVM Multimodal Person Verification

It has been shown that prosody helps to improve voice spectrum based speaker recognition systems. Therefore, prosodic features can also be used in multimodal person verification in order to achieve better results. In this paper, a multimodal recognition system based on facial and vocal tract spectral features is improved by adding prosodic information. Matcher weighting method and support vector machines have been used as fusion techniques, and histogram equalization has been applied before SVM fusion as a normalization technique. The results show that the performance of a SVM multimodal verification system can be improved by using histogram equalization, especially when the equalization is applied to those scores giving the highest EER values.

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