On the use of genuine-impostor statistical information for score fusion in multimodal biometrics

Matching score level fusion techniques in multimodal person verification conventionally use global score statistics in the normalization and fusion stages. In this paper novel normalization and fusion methods are presented to take advantage of the separate statistics of the monomodal scores in order to reduce the genuine and impostor PDF lobe overlapping and improve the verification rate. Joint mean normalization is an affine transformation that normalizes the mean of the monomodal biometrics scores separately for the genuine and impostor individuals. Histogram equalization is used to align the statistical distribution of the monomodal scores and make the whole separate statistics comparable. The presented weighting fusion methods have been designed to minimize the variances of the separate multimodal statistics and reduce the multimodal PDF lobe overlapping. The results obtained in speech and face scores fusion upon POLYCOST and XM2VTS databases show that the proposed techniques provide better results than the conventional methods.