Abstract
Linear discrimination analysis (LDA) technique is an important and well-developed area of image recognition and to date many linear discrimination methods have been put forward. Basically, in LDA the image always needs to be transformed into 1D vector, however recently two-dimensional PCA (2DPCA) technique have been proposed. In 2DPCA, PCA technique is applied directly on the original images without transforming into 1D vector. In this paper, we propose a new LDA-based method that applies the idea of two-dimensional PCA. In addition to that, our approach proposes an method called Discriminative Common Images based on a variation of Fisher’s LDA for face recognition. Experiment results show our method achieves better performance in comparison with the other traditional LDA methods.
This research was supported by the MIC (Ministry of Information and Communication), Korea, under the ITRC(Information Technology Research Center) support program supervised by the IITA (Institute of Information Technology Assessment). Corresponding Authors: Vo Dinh Minh Nhat (vo_dinhminhnhat@yahoo.com), and SungYoung Lee (sylee@oslab.khu.ac.kr).
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References
Turk, M.: A Pentland: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3, 71–86 (1991)
Zhao, W., Chellappa, R., Phillips, P.J.: Subspace Linear Discriminant Analysis for Face Recognition. Technical Report CAR-TR-914 (1999)
Swets, D.L., Weng, J.J.: Using discrimination eigenfeatures for image retrieval. IEEE Trans. Pattern Anal. Machine Intell. 18, 831–836 (1996)
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)
Yu, H., Yang, J.: A direct LDA algorithm for high-dimensional data with application to face recognition. Pattern Recognit. 34, 2067–2070 (2001)
Loog, M., Duin, R.P.W., Haeb-Umbach, R.: Multiclass linear dimension reduction by weighted pairwise fisher criteria. IEEE Trans. Pattern Anal. Machine Intell. 23, 762–766 (2001)
Martinez, A.M., Kak, A.C.: PCA versus LDA. IEEE Trans. Pattern Anal. Machine Intell. 23, 228–233 (2001)
Foley, D.H., Sammon, J.W.: An optimal set of discrimination vectors. IEEE Trans. Comput. C-24, 281–289 (1975)
Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. Eugenics 7, 178–188 (1936)
Huang, R., Liu, Q., Lu, H., Ma, S.: Solving the small sample size problem of LDA. In: Proceedings. 16th International Conference on Pattern Recognition, vol. 3 (2002)
Liu, C., Wechsler, H.: Robust coding scheme for indexing and retrieval from large face databases. IEEE Trans. Image Processing 9, 132–137 (2000)
Liu, C., Wechsler, H.: A shape- and texture-based enhanced Fisher classifier for face recognition. IEEE Trans. Image Processing 10, 598–608 (2001)
Chen, L., Liao, H.M., Ko, M., Lin, J., Yu, G.: A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recognit 33, 1713–1726 (2000)
Cevikalp, H., Neamtu, M., Wilkes, M., Barkana, A.: Discriminative common vectors for face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 4–13 (2005)
Yang, J., Zhang, D., Frangi, A.F., Yang, J.-y.: Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 131–137 (2004)
The Yale face database, http://cvc.yale.edu/projects/yalefaces/yalefaces.html
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Nhat, V.D.M., Lee, S. (2005). Discriminative Common Images for Face Recognition. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Biological Inspirations – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550822_88
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DOI: https://doi.org/10.1007/11550822_88
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