Abstract
This paper introduces a novel Gabor-based supervised locality preserving projection (GSLPP) method for face recognition. Locality preserving projection (LPP) is a recently proposed method for unsupervised linear dimensionality reduction. LPP seeks to preserve the local structure which is usually more significant than the global structure preserved by principal component analysis (PCA) and linear discriminant analysis (LDA). In this paper, we investigate its extension, called supervised locality preserving projection (SLPP), using class labels of data points to enhance its discriminant power in their mapping into a low dimensional space. The GSLPP method, which is robust to variations of illumination and facial expression, applies the SLPP to an augmented Gabor feature vector derived from the Gabor wavelet representation of face images. We performed comparative experiments of various face recognition schemes, including the proposed GSLPP method, principal component analysis (PCA) method, linear discriminant analysis (LDA) method, locality preserving projection method, the combination of Gabor and PCA method (GPCA) and the combination of Gabor and LDA method (GLDA). Experimental results on AR database and CMU PIE database show superior of the novel GSLPP method.
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References
Jolliffe, I.T.: Principal Component Analysis. Springer, New York (1986)
Klock, H., Buhmann, J.: Data visualization by multidimensional scaling: a deterministic annealing approach. Pattern Recognition 33(4), 651–669 (1999)
Belkin, M., Niyogi, P.: Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering. In: Proc. Conf. Advances in Neural Information Processing System, vol. 15 (2001)
Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401, 788–791 (1999)
Barlett, M.S., Ladesand, H.M., Sejnowsky, T.J.: Independent component representations for face recognition. In: Proc. SPIE, vol. 3299, pp. 528–539 (1998)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)
Tenenbaum, J.B., et al.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)
Zheng, Z., Yang, J.: Extended LLE with Gabor Wavelet for Face Recognition. In: Webb, G.I., Yu, X. (eds.) AI 2004. LNCS, vol. 3339, pp. 955–960. Springer, Heidelberg (2004)
He, X., Niyogi, P.: Locality Preserving Projections. In: Proc. Conf. Advances in Nerual Information Processing Systems (2003)
He, X., Yan, S., et al.: Face Recognition Using Laplacianfaces. IEEE trans. on PAMI 27(3), 328–340 (2005)
Zheng, X., Cai, D., He, X., Ma, W.-Y., Lin, X.: Locality Preserving Clustering for Image Database. In: ACM conference on Multimedia 2004, New York City, October 10-16 (2004)
He, X., Yan, S., Hu, Y., Zhang, H.-J.: Learning a Locality Preserving Subspace for Visual Recognition. In: IEEE International Conference on Computer Vision (ICCV 2003), Nice, France (2003)
de Ridder, D., Duin, R.P.W.: Locally linear embedding for classification. Technical Report PH-2002-01, Pattern Recogntion Group, Dept.of Imaging Science & Technology, Delft University of Technology, Delft, Netherlands (2002)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE tran. on PAMI 19(7), 711–721 (1997)
Zhao, W., Chellappa, R., Nandhakumar, N.: Empirical Performance Analysis of Linear Discriminant Classifers. In: Proc. Computer Vision and Pattern Recognition, pp. 164–169 (1998)
Swets, D.L., Weng, J.: Using Discriminant Eigenfeatures for Image Retrieval. IEEE trans. on PAMI 18(8), 831–836 (1996)
Cevikalp, H., Neamtu, M., et al.: Discriminant Common Vectors for Face Recognition. IEEE trans. on PAMI 27(1), 4–13 (2005)
Kim, T.K., Kittler, J.: Locally Linear Discriminant Analysis for Multimodally Distributed Classes for Face Recognition with a Single Model Image. IEEE trans. on PAMI 27(3), 318–327 (2005)
Yang, J., Frangi, A.F., Yang, J.-y., et al.: KPCA Plus LDA: A complete Kernel Fisher Discriminant Framework for Feature Extraction and Recognition. IEEE trans. on PAMIÂ 27(2) (2005)
Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive neuroscience 3, 71–86 (1991)
Cohen, I., Sebe, N., Cozman, F.G., Cirelo, M.C., Huang, T.S.: Learning Bayesian Network Classifiers for Facial Expression Recognition with both Labeled and Unlabeled data. In: IEEE conference on Computer Vision and Pattern Recognition 2003 (2003)
Graf, A.B.A., Smola, A.J., Borer, S.: Classification in a normalized feature space using support vector machines. IEEE trans. on PAMI 14(3), 597–605 (2003)
Li, R.-P., Mukaidono, M., Turksen, I.B.: A fuzzy neural network for pattern classification and feature selection. Fuzzy Sets and systems 130, 101–108 (2002)
Martinez, A.M., Benavente, R.: The AR face database, CVC Tech. Report #24 (1998)
Cohen, I., Cozman, F.G., Sebe, N., Cirelo, M.C., Huang, T.S.: Semi-supervised Learning of Classifiers: Theory, Algorithms, and Their application to Human-Computer Interaction. IEEE trans. on PAMI 26(12), 1553–1567 (2004)
Min, W., Lu, K., He, X.: Locality preserving projection. Pattern Recognition Journal 37(4), 781–788 (2004)
Donato, G., Bartlett, M.S., Hager, J.C., et al.: Classifying facial actions. IEEE Trans. Pattern Anal. Machine Intell. 21, 974–989 (1999)
Daugman, J.G.: Two-dimensional spectral analysis of cortical receptive field profiles. Vis. Res. 20, 847–856 (1980)
Liu, C., Wechsler, H.: A Gabor feature classifier for face recognition. In: Proc. 8th IEEE Int. Conf. Computer Vision, Vancouver, BC, Canada, July 9-12 (2001)
Hsu, R.-L., Mohamed, Jain, A.K.: Face Detection in Color Images. IEEE Trans. on PAMI 24, 696–706 (2002)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proc. Conf. Computer Vision and Pattern Recognition, Kauai, HI, USA, vol. 1, pp. 511–518 (2001)
Sim, T., Baker, S., Bsat, M.: The CMU Pose, Illumination, and Expression (PIE) Database. In: Proc. IEEE Int’l. Conf. Automatic Face and Gesture Recognition (2002)
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Zheng, Z., Zhao, J., Yang, J. (2006). Gabor Feature Based Face Recognition Using Supervised Locality Preserving Projection. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2006. Lecture Notes in Computer Science, vol 4179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11864349_59
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DOI: https://doi.org/10.1007/11864349_59
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