Combination of Experts by Classifiers in Similarity Score Spaces
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Designing multiple biometric systems: Measures of ensemble effectiveness
Engineering Applications of Artificial Intelligence
Dynamic Score Combination: A Supervised and Unsupervised Score Combination Method
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
A new framework for adaptive multimodal biometrics management
IEEE Transactions on Information Forensics and Security
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A biometric system for user authentication produces a matching score representing the degree of similarity of the input biometry 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 are evaluated in terms of the Receiver Operating Characteristic (ROC) curve, and the Equal Error Rate (EER). In order to increase the reliability of authentication through biometrics, the combination of different biometric systems is currently investigated by researchers. While a number of "fusion" algorithms have been proposed in the literature, in this paper we propose a theoretical analysis of a novel approach based on the "dynamic selection" of matching scores. Such a selector aims at choosing, for each user to be authenticated, just one of the scores produced by the different biometric systems available. We show that the "dynamic selection" of matching scores can provide a better ROC than those of individual biometric systems. Reported results on the FVC2004 dataset confirm the theoretical analysis, and show that the proposed "dynamic selection" approach is more effective when low quality scores are used.