Fusion of Multi-biometric Recognition Results by Representing Score and Reliability as a Complex Number

A critical element in multi-biometrics systems, is the rule to fuse the information from the different sources. The component sub-systems are often designed to further produce indices of input image quality and/or of system reliability. These indices can be used as weights assigned to scores (weighted fusion) or as a selection criterion to identify the subset of systems that actually take part in a single fusion operation. Many solutions rely on the estimation of the joint distributions of conditional probabilities of the scores from the single subsystems. The negative counterpart is that such very effective solutions require training and a high number of training samples, and also assume that score distributions are stable over time. In this paper we propose a unified representation of the score and of the quality/reliability index that simplifies the process of fusion, provides performance comparable to those currently offered by top performing schemes, yet does not require a prior estimation of score distributions. This is an interesting feature in highly dynamic systems, where the set of relevant subjects may undergo significant variations across time.