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.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. John Wiley & Sons, Chichester (2004)
Hong, L., Jain, A.K., Pankanti, S.: Can Multibiometrics Improve Performance? In: AutoID’99, pp. 59–64 (1999)
Ross, A., Nandakumar, K., Jain, A.: Handbook of Multibiometrics. Springer, Heidelberg (2006)
Huang, J., Ling, C.X.: Using AUC and Accuracy in Evaluating Learning Algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005)
Mann, H.B., Whitney, D.R.: On a test whether one or two random variable is stochastically larger than the other. Ann. Math. Statistic 18, 50–60 (1947)
Hanley, J.A., McNeil, B.J.: The meaning and the use of the area under a receiver operanting charateristic curve. Radiology 143, 29–36 (1982)
C., M., M., M., F., T.: Exploiting AUC for optimal linear combinations of dichotomizers. Pattern Recognition Letters 27(8), 900–907 (2006)
Roli, F., Giacinto, G., Tronci, R.: Score Selection Techniques for Fingerprint Multi-modal Biometric Authentication. In: Roli, F., Vitulano, S. (eds.) ICIAP 2005. LNCS, vol. 3617, pp. 1018–1025. Springer, Heidelberg (2005)
Jain, A.K., et al.: FVC2004: Third Fingerprint Verification Competition. In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS, vol. 3072, pp. 1–7. Springer, Heidelberg (2004)
FVC2004 Website: http://bias.csr.unibo.it/fvc2004/
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley & Sons, Chichester (2001)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Tronci, R., Giacinto, G., Roli, F. (2007). Index Driven Combination of Multiple Biometric Experts for AUC Maximisation. In: Haindl, M., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2007. Lecture Notes in Computer Science, vol 4472. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72523-7_36
Download citation
DOI: https://doi.org/10.1007/978-3-540-72523-7_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72481-0
Online ISBN: 978-3-540-72523-7
eBook Packages: Computer ScienceComputer Science (R0)