IEEE Transactions on Pattern Analysis and Machine Intelligence
Expert Conciliation for Multi Modal Person Authentication Systems by Bayesian Statistics
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
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
A Theoretical and Experimental Analysis of Linear Combiners for Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
Handbook of Multibiometrics (International Series on Biometrics)
Handbook of Multibiometrics (International Series on Biometrics)
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Quality-based Score Level Fusion in Multibiometric Systems
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Maximizing the area under the ROC curve by pairwise feature combination
Pattern Recognition
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
Incorporating image quality in multi-algorithm fingerprint verification
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Improving fusion with margin-derived confidence in biometric authentication tasks
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
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In two-class problems, the linear combination of the outputs (scores) of an ensemble of classifiers is widely used to attain high performance. In this paper we investigate some techniques aimed at dynamically estimate the coefficients of the linear combination on a pattern per pattern basis. We will show that such a technique allows providing better performance than those of static combination techniques, whose parameters are computed beforehand. The coefficients of the linear combination are dynamically computed according to the Wilcoxon-Mann-Whitney statistic. Reported results on a multi-modal biometric dataset show that the proposed dynamic mechanism allows attaining very low error rates when high level of precision are required.