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Improved $(2D)^2$ DLDA for Face Recognition  

Cho, Dong-Uk (충북과학대학 정보통신과학과)
Chang, Un-Dong (충북대학교 정보통신공학과)
Kim, Young-Gil (충북대학교 정보통신공학과)
Kim, Kwan-Dong (충북대학교 정보통신공학과)
Ahn, Jae-Hyeong (충북대학교 정보통신공학과)
Kim, Bong-Hyun (한밭대학교 정보통신전문대학원 컴퓨터공학과)
Lee, Se-Hwan (한밭대학교 정보통신전문대학원 컴퓨터공학과)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.31, no.10C, 2006 , pp. 942-947 More about this Journal
Abstract
In this paper, a new feature representation technique called Improved 2-directional 2-dimensional direct linear discriminant analysis (Improved $(2D)^2$ DLDA) is proposed. In the case of face recognition, thesmall sample size problem and need for many coefficients are often encountered. In order to solve these problems, the proposed method uses the direct LDA and 2-directional image scatter matrix. Moreover the selection method of feature vector and the method of similarity measure are proposed. The ORL face database is used to evaluate the performance of the proposed method. The experimental results show that the proposed method obtains better recognition rate and requires lesser memory than the direct LDA.
Keywords
Linear Discriminant Analysis; Direct IDA; Face Recognition;
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1 Wang, L., Wang, X., Zhang, X., Feng, J.: The equivalence of two-dimensional PCA to line-based PCA. Pattern Recognition Letters. Vol. 26. (2005) 57-60   DOI   ScienceOn
2 Turk, M., Pentalnd, A.: Eigenfaces for recognition. J. Cognitive Neurosci. Vol. 3. No. 1. (1991) 71-86   DOI   ScienceOn
3 Fukunaga, K.: Introduction to Statistical Pattern Recognition. 2nd edn. Academic Press, New York (1990)
4 Yang, J., Zhang, D., Frangi, A.F., Yang, J.: Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition. IEEE Trans. Pattern Analysis and Machine Intelligence. Vol. 26. No. 1. (2004) 131-137   DOI   ScienceOn
5 Li, M., Yuan, B.: 2D-LDA: A Statistical Linear Discriminant Analysis for Image Matrix. Pattern Recognition Letters. Vol. 26. (2005) 527-532   DOI   ScienceOn
6 Pentland, A., Moghaddam, B., Stamer, T.: View-based and modular eigenspaces for face Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. (1994) 84-91
7 Belhumeour, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans. Pattern Analysis and Machine Intelligence. Vol. 19. No.7. (1997) 711-720   DOI   ScienceOn
8 Yu, R., Yang, J.: A direct LDA algorithm for high-dimensional data with application to face recognition. Pattern Recognition. Vol. 34. No. 10. (2001) 2067-2070   DOI   ScienceOn