Rank level fusion in multibiometric systems

Multibiometric systems have recently become a preferred option for human identification over the unibiometric systems. It increases the recognition rate and confidence in the final decision, and simultaneously reduces the failure to enroll rate (FER). For identification mode, rank level fusion is a feasible option as incompatibility and normalization issues present at the score level fusion are not prominent at this level and also sufficient information is present to fuse as opposed to the decision level fusion. We propose an improvement in existing rank level fusion techniques using two levels of hierarchy. Series and parallel combinations are proposed to combine the output of various rank level fusion techniques. Two formulations of series and parallel combinations are extensively evaluated on multi-algorithm, multi-instance and multi-modal biometric systems created from three publicly available datasets: (i) NIST BSSR1 [1] multi-modal biometric score database, (ii) Face Recognition Grand Challenge V2.0 [2] and (iii) LG4000 [3] iris images.