Skip to Main Content
Combination functions typically used in biometric identification systems consider as input parameters only those matching scores which are related to a single person in order to derive a combined score for that person. We discuss how such methods can be extended to utilize the matching scores corresponding to all people. The proposed combination methods account for dependencies between scores output by any single participating matcher. Our experiments demonstrate the advantage of using such combination methods when dealing with a large number of classes, as is the case with biometric person identification systems. The experiments are performed on the National Institute of Standards and Technology BSSR1 dataset and the combination methods considered include the likelihood ratio, neural network, and weighted sum.