Abstract
Two Dimensional Linear Discrimination Analysis (2DLDA) is an effective feature extraction approach for face recognition, which manipulates on the two dimensional image matrices directly. However, some between-class distances in the projected space are too small and this may bring large error classification rates. In this paper we propose a new 2DLDA-based approach that can overcome such drawback in the 2DLDA. The proposed approach redefines the between-class scatter matrix by putting a weighting function based on the between-class distances, and this will balance the between-class distances in the projected space iteratively. In order to design an effective weighting function, the between-class distances are calculated and then used to iteratively change the between-class scatter matrix. Experimental results showed that the proposed approach can improve the recognition rates on the ORL database, the Yale database and the YaleB database in comparison with other 2DLDA variants.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Chen, F., An, S., Liu, W.: A New Face Recognition Approach to Boosting the Worst-case Performance. In: Huang, Y.-M.R., Xu, C., Cheng, K.-S., Yang, J.-F.K., Swamy, M.N.S., Li, S., Ding, J.-W. (eds.) PCM 2008. LNCS, vol. 5353, pp. 927–930. Springer, Heidelberg (2008)
Jing, X.-Y., Zhang, D., Tang, Y.-Y.: An improved LDA Approach. IEEE Transaction on Systems, Man, And Cybernetics—Part B: Cybernetics 34(5), 1942–1951 (2004)
Lotlikar, R., Kothari, R.: Fractional-Step Dimensionality Reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(6), 623–627 (2000)
Li, M., Yuan, B.: 2DLDA: A Novel Statistical Llinear Discriminate Analysis for Image Matrix. Pattern Recognition Lett. 26(25), 527–532 (2005)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherface: Recognition using class specific linear projection. IEEE Trans. Pattern Anal. Machine Intell. 19, 711–720 (1997)
Bing, Y., Lianfu, J., Ping, C.: A new LDA-based method for face recognition. In: Proceedings of 16th International Conference on Pattern Recognition, August 11-15, vol. 1, pp. 168–171 (2002)
Tang, E.K., Suganthan, P.N., Yao, X.: Generalized LDA Using Relevance Weighting and evolution strategy. In: Congress on Evolutionary Computation (CEC) 2004, June 19-23, vol. 2, pp. 2: 2230–2234 (2004)
Loog, M., Duin, R.P.W., Haeb-Umbach, R.: Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria. IEEE Transaction on Pattern Analysis and Machine Intelligence 23(7), 762–766 (2001)
Jarchi, D., Boostani, R.: A New Weighted LDA Method in Comparison to Some Versions of LDA. Proceedings of Word Academy of Science, Engineering and Technology 12(18), 233–238 (2006)
An, S., Liu, W., Venkatesh, S.: Face Recognition Using Kernel Ridge Regression. In: Proc. CVPR 2007 (2007)
Koren, Y., Carmel, L.: Robust linear dimensionality reduction. IEEE Trans. Visualization and Computer Graphics 10(4), 459–470 (2004)
Duda, R., Hart, P., Stork, D.: Pattern classification. Wiley, Chichester (2000)
Yale Univ. Face Database (2002), http://cvc.yale.edu/projects/yalefaces/yalefaces.html
AT&t Laboratories Cambridge. The ORL Database of Faces, http://www.cam-orl.co.uk/facedatabase.html
Xu, T., Lu, C., Liu, W.: The Matrix Form for Weighted Linear Discriminant Analysis and Fractional Linear Discriminant Analysis. In: ICMLC 2009 (accepted, 2009)
Li, M., Yuan, B.: 2D-LDA: A novel statistical linear discriminant analysis for image matrix. Pattern Recognition Letter 26(5), 527–532 (2005)
Belhumeur, P.N., Hespanha, J., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lu, C., An, S., Liu, W., Liu, X. (2009). An Innovative Weighted 2DLDA Approach for Face Recognition. In: Muneesawang, P., Wu, F., Kumazawa, I., Roeksabutr, A., Liao, M., Tang, X. (eds) Advances in Multimedia Information Processing - PCM 2009. PCM 2009. Lecture Notes in Computer Science, vol 5879. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10467-1_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-10467-1_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10466-4
Online ISBN: 978-3-642-10467-1
eBook Packages: Computer ScienceComputer Science (R0)