Abstract
Feature extraction is a crucial step for face recognition. In this paper, based on Isometric Projections (IsoP), a new feature extraction method called Uncorrelated Discriminant Isometric Projections (UDIsoP) is proposed for face recognition. The aim of UDIsoP is to preserve the within-class geometry structure by taking into account the class label information. Moreover, the features extracted via UDIsoP are statistically uncorrelated with minimum redundancy, which is desirable for many pattern analysis applications. Experiment results on the publicly available ORL and Yale face databases show that the proposed UDIsoP approach provides a better representation of the data and achieves much higher recognition accuracy.
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References
Zhao, W., Chellappa, R., Phillips, P.J., et al.: Face recognition: a literature survey. ACM Computer Surveys 35(4), 399–458 (2003)
Jolliffe, I.T.: Principal Component Analysis. Springer, New York (1986)
Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neuro-science 3(1), 71–86 (1991)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans. Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)
He, X., Yan, S., Hu, Y., Niyogi, P., Zhang, H.J.: Face Recognition Using Laplacianfaces. IEEE. Trans. Pattern Analysis and Machine Intelligence 27(3), 328–340 (2005)
Roweis, S., Saul, L.: Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science 290, 2323–2326 (2000)
Tenenbaum, J.B., de Silva, V., Langford, J.C.: A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science 290, 2319–2323 (2000)
Belkin, M., Niyogi, P.: Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering. In: Advances in Neural Information Processing Systems 14, Vancouver, British Columbia, Canada (2002)
Zhang, Z., Zha, H.: Principle Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Aligenment. SIAM J. Sci. Compute 26(1), 313–338 (2004)
Zhang, T.H., Li, X.L., Tao, D.C., Yang, J.: Local Coordinates Alignment (LCA): a Novel Method for Manifold Learning. International Journal of Pattern Recognition and Artifical Intelligence 22(4), 667–690 (2008)
Xiang, S.M., Nie, F.P., Zhang, C.S., Zhang, C.X.: Nonlinear Dimensionality Reduction with Local Spline Embedding. IEEE Trans. on Knowledge and Data Engineering 21(9), 1285–1298 (2009)
Cai, D., He, X.F., Han, J.W.: Isometric projection. In: Proceedings of the 22nd National Conference on Artificial Intelligence (AAAI 2007), Vancouver, Canada, July 22-26, pp. 528–533. AAAIPress, Menlo Park (2007)
Pang, Y., Zhang, L., Liu, Z., Yu, N., Li, H.: Neighborhood Preserving Projections (NPP): A Novel Linear Dimension Reduction Method. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3644, pp. 117–125. Springer, Heidelberg (2005)
He, X., Niyogi, P.: Locality Preserving Projections. In: Proc. Advances in Neural Informaion Processing System Conf. (2003)
Sun, S.Y., Zhao, H.T., Yang, H.J.: Discriminant Uncorrelated Locality Preserving Projection. In: Proc. of International Conference on Image and Signal Processing, pp. 1849–4852 (2010)
Wang, G.Q., Zhang, W.J., Liu, D.T.: Discriminant Uncorrelated Neighborhood Preserving Projections. Journal of Computational Information Systems 6(4), 1027–1035 (2010)
Liu, H., Yang, J.A., Wang, Y.: Feature Extraction of Acoustic Targets Using Uncorrelated Neighborhood Preserving Discriminant Projections. Journal of Computational Information Systems 6(8), 2691–2698 (2010)
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Ge, B., Shao, Y., Shu, Y. (2012). Uncorrelated Discriminant Isometric Projection for Face Recognition. In: Liu, C., Wang, L., Yang, A. (eds) Information Computing and Applications. ICICA 2012. Communications in Computer and Information Science, vol 307. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34038-3_20
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DOI: https://doi.org/10.1007/978-3-642-34038-3_20
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