Abstract
Kernel discriminant analysis (KDA) and the kernel principal component analysis (KPCA), which are the extension of the linear discriminant analysis (LDA) and the principal component analysis (PCA), respectively, from linear domain to nonlinear domain via the kernel trick, are two very popular nonlinear feature extraction methods. In this paper, we present a new feature extraction algorithm by combing KDA and KPCA, and then apply it to the face recognition task. The experimental results on Yale face dataset show that the proposed method can significantly improve the performance both KDA and KPCA.
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
Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Müller, K.-R.: Fisher Discriminant Analysis with Kernels. In: Proceeding of IEEE Neural Networks for Signal Processing Workshop, pp. 41–48 (1999)
Baudat, G., Anouar, F.: Generalized Discriminant Analysis Using a Kernel Approach. Neural Computation 12(10), 2385–2404 (2000)
Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. John Wiley & Sons Inc., New York (1973)
Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press Inc., London (1990)
Schölkopf, B., Smola, A.J., Müller, K.-R.: Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Computation 10(5), 1299–1319 (1998)
Duchene, J.: A Significant Plane for Two-class Discrimination Problems. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(4), 557–559 (1986)
Siedlecki, W., Siedlecka, K., Sklansky, J.: An Overview of Mapping Techniques for Exploratory Pattern Analysis. Pattern Recognition 21(5), 411–429 (1988)
Zheng, W., Zhao, L., Zou, C.: A Modified Algorithm for Generalized Discriminant Analysis. Neural Computation 16(6), 1283–1297 (2004)
Yang, M.H.: Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition using Kernel Methods. In: Proceedings of the Fifth International conference on Automatic Face and Gesture Recognition, pp. 215–220 (2002)
Samal, A., Iyengar, P.: Automatic Recognition and Analysis of Human Faces and Facial Expressions: a Survey. Pattern Recognition 25(1), 65–77 (1992)
Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face Recognition, a Literature Survey. ACM Computing Surveys 35(4), 399–458 (2000)
Turk, M.A., Pentland, A.P.: Eigenfaces for Recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition using Class Specific Linear Projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)
Lu, J., Plataniotis, K.N., Venetsanopoulos, A.N.: Face Recognition using Kernel Direct Discriminant Analysis Algorithms. IEEE Transactions on Neural Networks 14(1), 117–126 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zheng, W. (2006). KDA Plus KPCA for Face Recognition. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_13
Download citation
DOI: https://doi.org/10.1007/11760023_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
Online ISBN: 978-3-540-34438-4
eBook Packages: Computer ScienceComputer Science (R0)