Abstract
In this paper, by supposing a parametric Gaussian distribution over the image space (spanned by the row vectors of 2D image matrices) and a spherical Gaussian noise model for the image, we endow the two-dimensional principal component analysis (2DPCA) with a probabilistic framework called probabilistic 2DPCA (P2DPCA), which is robust to noise. Further, by using the probabilistic perspective of P2DPCA, we extend P2DPCA to a mixture of local P2DPCA models (MP2DPCA). MP2DPCA offers us a method of being able to model faces in unconstrained (complex) environment with possibly large variation. The model parameters could be fitted on the basis of maximum likelihood (ML) estimation via the expectation maximization (EM) algorithm. The experimental recognition results on UMIST face database confirm the effectivity of the proposed methods.
This work was partly funded by National Natural Science Foundation of China (Grant No. 10571001, 60503023, and 60375010), and partly by Jiangsu Natural Science Foundation (Grant No. BK2005407) and Program for New Century Excellent Talents in University (Grant No. NCET-05-0467).
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
Jolliffe, I.T.: Principal Component Analysis. Springer, New York (1986)
Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3, 71–86 (1991)
Zhao, W., Chellappa, R., Rosenfeld, A., Phillips, P.J.: Face recognition: a literature survey. Technical Report CAR-TR-948. Vision Technologies Lab, Sarnoff Corporation, Princeton (2000)
Yang, J., Zhang, D., Frangi, A.F., Yang, J.y.: Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans. Patt. Anal. Mach. Intell 26, 1–7 (2004)
Wang, L., Wang, X., Zhang, X., Feng, J.: The equivalence of two-dimensional PCA to line-based PCA. Pattern Recognition Lett. 26, 57–60 (2005)
Kong, H., Wang, L., Teoh, E.K., Li, X., Wang, J.G., Venkateswarlu, R.: Generalized 2D principal component analysis for face image representation and recognition. Neural Networks 18, 585–594 (2005)
Tipping, M.E., Bishop, C.M.: Mixtures of probabilistic principal component analysers. Neural Comput. 11, 443–482 (1999)
McLachlan, G.J., Krishnan, T.: The EM Algorithm and Extensions. Wiley, New York (1997)
Graham, D.B., Allinson, N.M.: Characterizing virtual eigensignatures for general purpose face recognition. In: Wechsler, H., Phillips, P.J., Bruce, V., Fogelman-Soulie, F., Huang, T.S. (eds.) Face Recognition: From Theory to Applications, NATO ASI Series F, Computer and Systems Sciences, vol. 163, pp. 446–456 (1998)
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
Wang, H., Hu, Z. (2006). Face Recognition Using Probabilistic Two-Dimensional Principal Component Analysis and Its Mixture Model. In: Jiao, L., Wang, L., Gao, Xb., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881070_48
Download citation
DOI: https://doi.org/10.1007/11881070_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45901-9
Online ISBN: 978-3-540-45902-6
eBook Packages: Computer ScienceComputer Science (R0)