Exploiting cohorts information in multibiometric identification systems via graph-based fusion

A biometric identification system determines the identity of a given biometric input data, among a set of gallery identities stored in the database. The existing identification techniques typically base the decision on the match scores representing the similarity between the query and the template of each gallery. The strategies proposed so far for combining different biometric identification systems usually use only the rank one identity unimodal output in order to compute the integrated evidence of the i_th person. In this paper, we propose a novel framework for fusion at hybrid rank-score level based on graph models where an additional information concerning the non-matched scores is exploited. Experiments were carried out on two multimodal databases and they demonstrate the effectiveness of the proposed approach for identification performance improvement.