Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Using AUC and Accuracy in Evaluating Learning Algorithms
IEEE Transactions on Knowledge and Data Engineering
Handbook of Multibiometrics (International Series on Biometrics)
Handbook of Multibiometrics (International Series on Biometrics)
Exploiting AUC for optimal linear combinations of dichotomizers
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Score selection techniques for fingerprint multi-modal biometric authentication
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Designing multiple biometric systems: Measures of ensemble effectiveness
Engineering Applications of Artificial Intelligence
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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.