Abstract
Traditional face recognition algorithms are mostly based on vector space. These algorithms result in the curse of dimensionality and the small-size sample problem easily. In order to overcome these problems, a new discriminant orthogonal rank-one tensor projections algorithm is proposed. The algorithm with tensor representation projects tensor data into vector features in the orthogonal space using rank-one projections and improves the class separability with the discriminant constraint. Moreover, the algorithm employs the alternative iteration scheme instead of the heuristic algorithm and guarantees the orthogonality of rank-one projections. The experiments indicate that the algorithm proposed in the paper has better performance for face recognition.
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References
Vasilescu, M., Terzopoulos, D.: Multilinear analysis of image ensembles: TensorFaces. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 447–460. Springer, Heidelberg (2002)
Tao, D., Li, X., Wu, X., Hu, W., Maybank, S.: Supervised tensor learning. Knowledge and Information Systems 13, 1–42 (2007)
Lu, H., Plataniotis, K., Venetsanopoulos, A.: MPCA: Multilinear Principal Component Analysis of Tensor Objects. IEEE Transactions on Neural Networks 19, 18–39 (2008)
Zafeiriou, S.: Discriminant Nonnegative Tensor Factorization Algorithms. IEEE Transactions on Neural Networks 20, 217–235 (2009)
Lu, H., Plataniotis, K., Venetsanopoulos, A.: Gait recognition through MPCA plus LDA. In: Proceedings of the Biometric Consortium Conference, Baltimore, MD, USA, pp. 1–6 (2006)
Yan, S., Xu, D., Yang, Q., Zhang, L., Tang, X., Zhang, H.: Multilinear Discriminant Analysis for Face Recognition. IEEE Transactions On Image Processing 16, 212–220 (2007)
Tao, D., Li, X., Wu, X., Maybank, S.: General Tensor Discriminant Analysis and Gabor Features for Gait Recognition. IEEE Transactions On Pattern Analysis And Machine Intelligence 29, 1700–1715 (2007)
Tao, D., Li, X., Wu, X., Maybank, S.: Tensor Rank One Discriminant Analysis–A convergent method for discriminative multilinear subspace selection. Neurocomputing 71, 1866–1882 (2008)
Lu, H., Plataniotis, K., Venetsanopoulos, A.: Uncorrelated multilinear discriminant analysis with regularization and aggregation for tensor object recognition. IEEE Transactions On Neural Networks 20, 103–123 (2009)
Lu, H., Plataniotis, K., Venetsanopoulos, A.: A taxonomy of emerging multilinear discriminant analysis solutions for biometric signal recognition, Biometrics: Theory, Methods, and Applications, pp. 21–45. Wiley-IEEE (2009)
Kokiopoulou, E., Saad, Y.: Orthogonal neighborhood preserving projections: A projection-based dimensionality reduction technique. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 2143–2156 (2007)
Zhang, T., Huang, K., Li, X., Yang, J., Tao, D.: Discriminative orthogonal neighborhood-preserving projections for classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 40, 253–263 (2010)
Lee, K., Ho, J., Kriegman, D.: Acquiring linear subspaces for face recognition under variable lighting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 684–698 (2005)
Georghiades, A., Belhumeur, P., Kriegman, D.: From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose. IEEE Transactions on Pattern analysis and Machine Intelligence, 643–660 (2001)
Ye, J.: Characterization of a family of algorithms for generalized discriminant analysis on undersampled problems. Journal of Machine Learning Research 6, 483–502 (2005)
Nanni, L., Lumini, A.: Orthogonal linear discriminant analysis and feature selection for micro-array data classification. Expert Systems with Applications 37, 7132–7137 (2010)
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Liu, C., He, K., Zhou, Jl., Gao, CB. (2011). Discriminant Orthogonal Rank-One Tensor Projections for Face Recognition. In: Nguyen, N.T., Kim, CG., Janiak, A. (eds) Intelligent Information and Database Systems. ACIIDS 2011. Lecture Notes in Computer Science(), vol 6592. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20042-7_21
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DOI: https://doi.org/10.1007/978-3-642-20042-7_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20041-0
Online ISBN: 978-3-642-20042-7
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