Abstract
Subspace learning is one of the main directions for face recognition. In this paper, a novel unsupervised subspace learning method, Neighborhood Preserving Projections (NPP), is proposed. In contrast to traditional linear dimension reduction method, such as principal component analysis (PCA), the proposed method has good neighborhood-preserving property. The central idea is to modify the classical locally linear embedding by introducing a linear transform matrix. The transform matrix is obtained by optimizing a certain objective function. Experimental results on Yale face database and FERET face database show the effectiveness of the proposed method....
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Zhao, W., Chellappa, R., Rosenfeld, A., Phillips, J.: Face recognition: A Literature Survey. ACM Computing Surveys 12, 399–458 (2003)
Samaria, F.S.: Face Recognition Using Hidden Markov Models. PhD thesis, University of Cambridge (1994)
Wiskott, L., Fellous, J., Kruger, N., Malsburg, C.: Face Recognition by Elastic Bunch Graph Matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 775–779 (1997)
Penev, P.S.: Local Feature Analysis: a General Statistical Theory for Object Repentation. Network: Computation in Neural Systems 7, 477–500 (1996)
Turk, M., Pentland, A.: Face Recognition Using Eigenfaces. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 586–591 (1991)
Peter, N.B., Joao, P.H., David, J.K.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection 19(7), 711–720 (1997)
Moghaddam, B., Pentland, A.: Probabilistc Visual Learning For Object Reprentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 696–710 (1997)
Moghaddam, B., Jebara, B., Pentland, A.: Bayesian Face Recognition. Pattern Recognition 33, 1771–1782 (2000)
Wang, X., Tang, X.: Unified Subspace Analysis for Face Recognition. In: Proc. IEEE Conference on Computer Vision (2003)
He, X., Yan, S., Hu, Y., Niyogi, P., Zhang, H.J.: Face Recognition Using Laplacianfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(3), 328–340 (2005)
Roweis, S., Saul, L.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)
Saul, L.K., Roweis, S.T.: Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifolds. Journal of Machine Learning Research 4, 119–155 (2003)
Phillips, P.J., Moon, H., Rivzi, S., Rauss, P.: The FERET evaluation methodology for face-recognition algorithms. IEEE Transaction on Pattern Analysis and Machine Intelligence 22, 1090–1104 (2000)
He, X., Yan, S., Hu, Y., Zhang, H.: Learning alocality preserving subspace for visual recognition. In: IEEE International Conference on Computer Vision, vol. I, pp. 385–392 (2003)
Tipping, M., Bishop, C.: Probabilistic principal component analysis. Journal of the Royal Statistical Society 61(3), 611–622 (1999)
Belkin, M., Niyogi, P.: Using manifold structure for partially labeled classification. Advances in Neural Information Processing System 15 (2002)
Blum, A., Chawla, S.: Learning from labeled and unlabeled data using graph mincuts. In: International Conference on Machine Learning, pp. 19–26 (2001)
John, S.T., Nello, C.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)
Scholkopf, B., Smola, A., Muller, K.: Nonlinear component analysis as a kernel eigenvale problem. Neural Computation 10(5), 1299–1319 (1998)
Bengio, Y., Paiemetn, J., Vincent, P.: Out-of-sample extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering. Advances in Neural Information Processing System (2003)
Yu, H., Yang, J.: A direct LDA algorithm for high dimensional data with application to face recognition. Pattern Recognition 34, 2067–2070 (2001)
Wang, X., Tang, X.: Dual-space linear discriminant analysis for face recognition. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (2004)
Wang, X., Tang, X.: Unified subspace analysis for face recognition. In: Proceedings of the Ninth IEEE International Conference on Computer Vision (ICCV), vol. 1(1), pp. 679–686 (2003)
Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recogntion: a literature survey. ACM Computing 35(4), 399–458 (2003)
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Pang, Y., Yu, N., Li, H., Zhang, R., Liu, Z. (2005). Face Recognition Using Neighborhood Preserving Projections. In: Ho, YS., Kim, HJ. (eds) Advances in Multimedia Information Processing - PCM 2005. PCM 2005. Lecture Notes in Computer Science, vol 3768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11582267_74
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DOI: https://doi.org/10.1007/11582267_74
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30040-3
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