Index driven combination of multiple biometric experts for AUC maximisation

Abstract

A biometric system produces a matching score representing the degree of similarity of the input with the set of templates for that user. If the score is greater than a prefixed threshold, then the user is accepted, otherwise the user is rejected. Typically, the performance is evaluated in terms of the Receiver Operating Characteristic (ROC) curve, where the correct acceptance rate is plotted against the false authentication rate. A measure used to characterise a ROC curve is the Area Under the Curve (AUC), the larger the AUC, the better the ROC. In order to increase the reliability of authentication through biometrics, the combination of different biometric systems is currently investigated by researchers. In this paper two open problems are addressed: the selection of the experts to be combined and their related performance improvements. To this end we propose an index to be used for the experts selection to be combined, with the aim of the AUC maximisation. Reported results on FVC2004 dataset show the effectiveness of the proposed index. © Springer-Verlag Berlin Heidelberg 2007.