Abstract
Bayesian subspace analysis (BSA) has been successfully applied in data mining and pattern recognition. However, due to the use of probabilistic measure of similarity, it often needs much more projective vectors for better performance, which makes the compression ratio very low. In this paper, we propose a novel 2D Bayesian subspace analysis (2D-BSA) method for face recognition at high compression ratios. The main difference between the proposed 2D-BSA and BSA is that the former adopts a new Image-as-Matrix representation for face images, opposed to the Image-as-Vector representation in original BSA. Based on the new representation, 2D-BSA seeks two coupled set of projective vectors corresponding to the rows and columns of the difference face images, and then use them for dimensionality reduction. Experimental results on ORL and Yale face databases show that 2D-BSA is much more appropriate than BSA in recognizing faces at high compression ratios.
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References
Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans. Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)
Kong, H., Wang, L., Teoh, E.K., Wang, J.G., Venkateswarlu, R.: A Framework of 2D Fisher Discriminant Analysis: Applications to Face Recognition with Small Number of Training Samples. In: IEEE Conf. CVPR (2005)
Moghaddam, B., Pentland, A.: Probabilistic Visual Learning for Object Representation. IEEE Trans. Pattern Analysis and Machine Intelligence 19(7), 696–710 (1997)
Moghaddam, B., Jebara, T., Pentland, A.: Bayesian Face Recognition. Pattern Recognition 33(11), 1771–1782 (2000)
Turk, M.A., Pentland, A.P.: Face Recognition Using Eigenfaces. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 586–591 (1991)
Zhang, D., Chen, S., Liu, J.: Representing image matrices: Eigenimages versus eigenvectors. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3497, pp. 659–664. Springer, Heidelberg (2005)
Zhang, D., Chen, S., Zhou, Z.-H.: Recognizing Face or Object from a Single Image: Linear vs. Kernel Methods on 2D Patterns. In: Yeung, D.-Y., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds.) SSPR 2006 and SPR 2006. LNCS, vol. 4109, pp. 889–897. Springer, Heidelberg (2006)
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Zhang, D. (2007). Two-Dimensional Bayesian Subspace Analysis for Face Recognition. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_93
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DOI: https://doi.org/10.1007/978-3-540-72393-6_93
Publisher Name: Springer, Berlin, Heidelberg
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