Belief Function Theory Based Biometric Match Score Fusion: Case Studies in Multi-instance and Multi-unit Iris Verification

Abstract

This paper presents a framework for multi-biometric match score fusion when non-ideal conditions cause conflict in the results of different classifiers. The proposed framework uses belief function theory to effectively fuse the match scores and density estimation technique to compute the belief assignments. Fusion is performed using belief models such as Transferable Belief Model (TBM) and Proportional Conflict Redistribution (PCR) Rule followed by the likelihood ratio based decision making. Experimental results on multi-instance and multi-unit iris verification show that the proposed fusion framework with PCR rule yields the best verification accuracy even when individual biometric classifiers provide highly conflicting match scores.