Abstract
Statistical dependence of classifiers has recently been shown to improve accuracy over statistically independent classifiers. In this paper, we focus on the verification application and theoretically analyze the AND fusion rule to find the favorable conditional dependence that improves the fusion accuracy over conditionally independent classifiers. Based on this analysis, we come with a method to design such classifiers by training the classifiers on different partitions of the training data. The AR face database is used for performance evaluation and the proposed method has a false rejection rate (FRR) of 2.4% and a false acceptance rate of 3.3% on AND fusion, which is better than an FRR of 3.8% and FAR of 4.3% when classifiers are designed without taking account the AND fusion rule.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Phillips, P.J., et al: FRVT 2002: Evaluation Report (2003)
Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On Combining Classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(3), 226–239 (1998)
Varshney, P.K.: Distributed detection and data fusion. Springer, New York (1997)
Xu, L., et al.: Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Trans. Systems, Man & Cybernetics 22(3) (1992)
Ho, T.K., et al.: Decision combination in multiple classifier systems. IEEE Trans. PAMI 16(1), 66–75 (1994)
Giacinto, G., Roli, F.: An approach to the automatic design of multiple classifier systems. Pattern recognition letters 22, 25–33 (2001)
Kleinberg, E.M.: An overtraining-resistant stochastic modeling method for pattern recognition. The annals of statistics 24(6), 2319–2349 (1996)
Shin, H.W., Sohn, S.Y.: Selected tree classifier combination based on both accuracy and error diversity. Pattern recognition 38, 191–197 (2005)
Hastie, T.: The elements of statistical learning. Springer, New York (2001)
Kuncheva, L.I., Whitaker, C.J., Shipp, C.A., Duin, R.P.W.: Limits on the majority vote accuracy in classifier fusion. Pattern Analysis and Applications 6, 22–31 (2003)
Martinez, A.M., Benavente, R.: The AR Face Database. CVC Technical Report #24 (1998)
Zheng, W., Zou, C., Zhao, L.: Real-time face recognition using Gram-Schmidt orthogo-nalization for LDA. In: ICPR, pp. 403-406 (2004)
Kuncheva, L.I., Whitaker, C.J.: Measures of diversity in classifier ensembles. Machine Learning 51, 181–207 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Venkataramani, K., Kumar, B.V.K.V. (2005). Conditionally Dependent Classifier Fusion Using AND Rule for Improved Biometric Verification. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Image Analysis. ICAPR 2005. Lecture Notes in Computer Science, vol 3687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552499_31
Download citation
DOI: https://doi.org/10.1007/11552499_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28833-6
Online ISBN: 978-3-540-31999-3
eBook Packages: Computer ScienceComputer Science (R0)