Confidence based rank level fusion for multimodal biometric systems

Abstract

Multimodal biometric systems have proven advantages over single biometric systems as they are using multiple traits of users. The intra-class variance provided by using more than one trait results in a high identification rate. Still, one of the missing parts in a multimodal system is inattention to the discriminability of each rank list for each specific user. This paper introduces a novel approach to select a combination of rank lists in rank level so that it provides the highest discrimination for any specific query. The rank list selection is based on pseudo-scores lists that are created by combination of rank lists and resemblance probability distribution of users. The experimental results on a multimodal biometric system based on frontal face, profile face, and ear indicated higher identification rate by using novel confidence based rank level fusion.