In a multimodal biometric system, the effective fusion method is necessary for combining information from various single modality systems. In this paper we examined the performance of sum rule-based score level fusion and Support Vector Machines (SVM)-based score level fusion. Three biometric characteristics were considered in this study: fingerprint, face, and finger vein. We also proposed a new robust normalization scheme (Reduction of High-scores Effect normalization) which is derived from min-max normalization scheme. Experiments on four different multimodal databases suggest that integrating the proposed scheme in sum rule-based fusion and SVM-based fusion leads to consistently high accuracy.
['Shi-Jinn Horng', 'Yuan-Hsin Chen', 'Ray-Shine Run', 'Rong-Jian Chen', 'Jui-Lin Lai', 'Kevin Octavius Sentosal']
An Improved Score Level Fusion in Multimodal Biometric Systems