Abstract
The two-dimensional Principal Component Analysis (2DPCA) is a robust method in face recognition. Much recent research shows that the 2DPCA is more reliable than the well-known PCA method in recognising human face. However, in many cases, this method tends to be overfitted to sample data. In this paper, we proposed a novel method named random subspace two-dimensional PCA (RS-2DPCA), which combines the 2DPCA method with the random subspace (RS) technique. The RS-2DPCA inherits the advantages of both the 2DPCA and RS technique, thus it can avoid the overfitting problem and achieve high recognition accuracy. Experimental results in three benchmark face data sets − the ORL database, the Yale face database and the extended Yale face database B − confirm our hypothesis that the RS-2DPCA is superior to the 2DPCA itself.
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References
Kirby, M., Sirovich, L.: Application of the KL procedure for the characterization of human faces. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(1), 103–108 (1990)
Sirovich, L., Kirby, M.: Low-dimensional procedure for characterization of human faces. Journal of the Optical Society of America 4, 519–524 (1987)
Turk, M., Pentland, A.: Eigenfaces for recognition. Cognitive Neuroscience 3(1), 71–86 (1991)
Kramer, M.A.: Nonlinear principle component analysis using auto-associative Neural networks. American Institution Chemical Engineering Journal 32(2) (1991)
Yunen, P., Lai, J.: Face representation using independent component analysis. Pattern recognition 35, 1247–1257 (2002)
Yang, M., Ahuja, N., Kriegman, D.: Face recognition using kernel Eigenfaces. In: Proceedings of International Conference of Image Processing, vol. 1, pp. 37–40 (2000)
Yang, J., Zhang, D., Frangi, A.F., Yang, J.: Two-dimensional PCA: A new approach to appearance-based face representation and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(1), 131–137 (2004)
Kong, H., Wang, L., Teoh, E.K., Li, X., Wang, J., Venkateswarlu, R.: Generalized 2D principal component analysis for face image representation and recognition. Neural Networks 18, 585–594 (2005)
Visani, M., Garcia, C., Laurent, C.: Comparing robustness of two-dimensional PCA and Eigenfaces for face recognition. In: Campilho, A., Kamel, M. (eds.) ICIAR 2004. LNCS, vol. 3211, pp. 717–724. Springer, Heidelberg (2004)
Wang, L., Wang, X., Zhang, X., Feng, J.: The equivalence of two-dimensional PCA to line-based PCA. Pattern Recognition Letters 26(1), 57–60 (2005)
Ho, T.K.: The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 10(8), 832–844 (1998)
Wang, X., Tang, X.: Random sampling for subspace face recognition. International Journal of Computer Vision 70(1), 91–104 (2006)
Belhumeur, P., Hespanda, J., Kiregeman, D.: Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)
Chen, L., Liao, H., Ko, M., Liin, J., Yu, G.: A new LDA-based face recognition system which can solve the small sample size problem. Pattern recognition 33(10), 1713–1726 (2000)
Georghiades, A., Belhumeur, P., Kriegman, D.: From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intelligence 23(6), 643–660 (2001)
Lee, K., Ho, J., Kriegman, D.: Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans. Pattern Anal. Mach. Intelligence 27(5), 684–698 (2005)
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Nguyen, N., Liu, W., Venkatesh, S. (2007). Random Subspace Two-Dimensional PCA for Face Recognition. In: Ip, H.HS., Au, O.C., Leung, H., Sun, MT., Ma, WY., Hu, SM. (eds) Advances in Multimedia Information Processing – PCM 2007. PCM 2007. Lecture Notes in Computer Science, vol 4810. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77255-2_81
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DOI: https://doi.org/10.1007/978-3-540-77255-2_81
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