Abstract
This paper presents a novel scheme for face feature extraction, namely, the generalized two-dimensional Fisher’s linear discriminant (G-2DFLD) method. The G-2DFLD method is an extension of the 2DFLD method for feature extraction. Like 2DFLD method, G-2DFLD method is also based on the original 2D image matrix. However, unlike 2DFLD method, which maximizes class separability either from row or column direction, the G-2DFLD method maximizes class separability from both the row and column directions simultaneously. In G-2DFLD method, two alternative Fisher’s criteria have been defined corresponding to row and column-wise projection directions. The principal components extracted from an image matrix in 2DFLD method are vectors; whereas, in G-2DFLD method these are scalars. Therefore, the size of the resultant image feature matrix is much smaller using G-2DFLD method than that of using 2DFLD method. The proposed G-2DFLD method was evaluated on two popular face recognition databases, the AT&T (formerly ORL) and the UMIST face databases. The experimental results show that the new G-2DFLD scheme outperforms the PCA, 2DPCA, FLD and 2DFLD schemes, not only in terms of computation times, but also for the task of face recognition using a multi-class support vector machine.
Keywords
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
Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, New York (1990)
Er, M.J., Wu, S., Lu, J., Toh, H.L.: Face recognition with radial basis function (RBF) neural networks. IEEE Trans. Neural Networks 13, 697–710 (2002)
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. Pattern Anal. Mach. Intell. 26, 131–137 (2004)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces versus fisherfaces: Recognition using class specific linear projection. IEEE Trans. Pattern Anal. Machine Intell. 19, 711–720 (1997)
Xiong, H., Swamy, M.N.S., Ahmad, M.O.: Two-dimensional FLD for face recognition. Pattern Recognition 38, 1121–1124 (2005)
ORL face database. AT&T Laboratories, Cambridge, U. K., http://www.uk.research.att.com/facedatabase.html
Graham, D.B., Allinson, N.M.: Characterizing Virtual Eigensignatures for General Purpose Face Recognition: From Theory to Applications. In: Wechsler, H., Phillips, P.J., Bruce, V., Fogelman-Soulie, F., Huang, T.S. (eds.) Computer and Systems Sciences. NATO ASI Series F, vol. 163, pp. 446–456 (1998)
Vapnik, V.N.: Statistical learning theory. John Wiley & Sons, New York (1998)
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
Chowdhury, S., Sing, J.K., Basu, D.K., Nasipuri, M. (2010). Generalized Two-Dimensional FLD Method for Feature Extraction: An Application to Face Recognition. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2010. Lecture Notes in Computer Science(), vol 6119. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13672-6_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-13672-6_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13671-9
Online ISBN: 978-3-642-13672-6
eBook Packages: Computer ScienceComputer Science (R0)