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.
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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)
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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
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DOI: https://doi.org/10.1007/978-3-642-13672-6_11
Publisher Name: Springer, Berlin, Heidelberg
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