Score level normalization and fusion of iris recognition

Biometric traits such as an iris texture is one of the dependable physiological biometric traits because of its uniqueness. In this paper, we explore a different approach of matching score fusion and the effect of normalization method to the fusion process. Despite a plenty of work of iris recognition methods have been proposed in recent years, many are paying attention to the feature extraction process and classification method. Less number of method focuses on the information fusion of iris images. Fusion is believed to produce a better discrimination power due to the rich information can be utilized from both of iris images. We conduct an analysis to investigate which fusion rule is able to produce the best result for iris recognition system. Experimental analysis using CASIA dataset shows sum rule fusion produces 99% recognition accuracy. The verification analysis shows the best result is GAR = 95% at the FRR = 0.1% when using min-max normalization method to preprocess the matching score before the fusion process.