Abstract
Classification algorithms based on template matching are used in many applications (e.g., face recognition). Performances of template matching classifiers are obviously affected by the representativeness of available templates. In many real applications, such representativeness can substantially decrease over the time (e.g., due to “aging” effects in biometric applications). Among algorithms which have been recently proposed to deal with such issue, the template co-update algorithm uses the mutual help of two complementary template matchers to update the templates over the time in a semi-supervised way [8]. However, it can be shown that the template co-update algorithm derives from a more general framework which supports the use of more than two template matching classifiers. The aim of this paper is to point out this fact and propose the co-update of multiple matchers. Preliminary experimental results are shown to validate the proposed model.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Duda, R., Hart, P., Stork, D.: Pattern Classification. Wiley-Interscience, Hoboken (2000)
Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of fingerprint recognition. Springer, Heidelberg (2003)
Li, S.Z., Jain, A.K. (eds.): Handbook of face recognition. Springer, Heidelberg (2005)
Ross, A., Nandakumar, K., Jain, A.K.: Handbook of Multibiometrics. Springer, Heidelberg (2006)
Zhu, X.: Semi-supervised learning literature survey, Technical report, Computer Sciences TR 1530, Univ. Wisconsis, Madison, USA (2006)
Roli, F., Marcialis, G.L.: Semi-supervised PCA-based face recognition using self-training. In: Yeung, D.-Y., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds.) SSPR 2006 and SPR 2006. LNCS, vol. 4109, pp. 560–568. Springer, Heidelberg (2006)
Didaci, L., Marcialis, G.L., Roli, F.: Modelling FRR of Biometric Verification Systems using the Template Co-update Algorithm. In: 3rd IAPR/IEEE Int. Conference on Biometrics ICB 2009, Alghero (Italy), June, 2-5. LNCS, Springer, Heidelberg (2009) (in press)
Roli, F., Didaci, L., Marcialis, G.L.: Adaptive biometric systems that can improve with use. In: Ratha, N., Govindaraju, V. (eds.) Advances in Biometrics: Sensors, Systems and Algorithms, pp. 447–471. Springer, Heidelberg (2008)
Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proc. of the Workshop on Computational Learning Theory, pp. 92–100 (1998)
Kittler, J., Hatef, M., Duin, R., Matas, J.: On Combining Classifiers. IEEE Trans. On PAMI 20(3), 226–239 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Didaci, L., Marcialis, G.L., Roli, F. (2009). Semi-supervised Co-update of Multiple Matchers. In: Benediktsson, J.A., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2009. Lecture Notes in Computer Science, vol 5519. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02326-2_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-02326-2_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02325-5
Online ISBN: 978-3-642-02326-2
eBook Packages: Computer ScienceComputer Science (R0)