Abstract
Recently proposed Marginal Fisher Analysis (MFA), as one of the manifold learning methods, has obtained better classification results than the conventional subspace analysis methods and other manifold learning algorithms such as ISOMAP and LLE, because of its ability to find the intrinsic structure of data space and its nature of supervised learning as well. In this paper, we first propose a Gabor-based Marginal Fisher Analysis (GMFA) approach for face feature extraction, which combines MFA with Gabor filtering. The GMFA method, which is robust to variations of illumination and facial expression, applies the MFA to augmented Gabor feature vectors derived from the Gabor wavelet representation of face images. Then, the GMFA method is integrated with the Error Correction SVM classifier to form a novel face recognition system. We performed comparative experiments of various face recognition approaches on ORL database and FERET database. Experimental results show superiority of the GMFA features and the new recognition system presented in the paper.
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
Jolliffe, I.: Principal Component Analysis. Springer, New York (1986)
Etemad, K., Chellapa, R.: Discriminant analysis for recognition of human face images. J. Opt. Am. A 14(8), 1724–1733 (1997)
Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)
Saul, L.K., Roweis, S.T.: Think globally, fit locally: unsupervised learning of low dimensional manifolds. J. Mach. Learn. Res. (4), 119–155 (2003)
Yan, S., Xu, D., Zhang, B., Zhang, H.J.: Graph embedding: A General Framework for Dimensionality Reduction. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 830–837 (2005)
Chui, C.K.: An introduction to wavelets. Academic, Boston (1992)
Jones, J., Palmer, L.: An Evaluation of the Two-Dimensional Gabor Filter Model of Simple Receptive Fields in Cat Striate Cortex. J. Neurophysiology 58(6), 1233–1258 (1987)
Liu, C., Wechsler, H.: Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans. Image Processing 11(4), 467–476 (2002)
Liu, C.: Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition. IEEE Trans. Pattern Analysis and Machine Intelligence 26(5), 572–581 (2004)
Vapnik, V.: Statistical Learning Theory. John Willey and Sons Inc., New York (1998)
Kreßel, U.: Pairwise Classification and Support Vector Machines. In: Schölkopr, B., Burges, J.C., Smola, A.J. (eds.) Advances in Kernel Methods: Support Vector Learning. MIT Press, Cambridge (1999)
Sebald, D.J., Bucklew, J.A.: Support Vector Machines and Multiple Hypothesis Test Problem. IEEE Trans. on Signal Processing 49(11), 2865–2872 (2001)
Wang, C., Guo, C.: An SVM Classification Algorithm with Error Correction Ability Applied to Face Recognition. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3971, pp. 1057–1062. Springer, Heidelberg (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, C., Guo, C. (2010). Face Recognition Based on Gabor-Enhanced Manifold Learning and SVM. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13318-3_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-13318-3_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13317-6
Online ISBN: 978-3-642-13318-3
eBook Packages: Computer ScienceComputer Science (R0)