Variance reduction by genuine-impostor statistics in m

In this paper, we propose some novel normalization and fusion techniques for biometric matching score level fusion in person verification. While conventional matching score level fusion methods use global score statistics, we consider in this work both genuine and impostor statistics separately. Performing a joint mean normalization of the separate monomodal scores, multimodal scores with less separate variance than the monomodal ones are obtained. Furthermore, a weighting method has been designed in order to minimize the variance sum of the separate multimodal statistics. This method obtains a minor sum of genuine and impostor variances for the multimodal biometric than that of the monomodal ones. The results obtained in speech and face scores fusion upon POLYCOST and XM2VTS databases show that the proposed normalization and fusion techniques provide better results than the conventional methods.