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Face Recognition Using Laplacianfaces
March 2005 (vol. 27 no. 3)
pp. 328-340
We propose an appearance-based face recognition method called the Laplacianface approach. By using Locality Preserving Projections (LPP), the face images are mapped into a face subspace for analysis. Different from Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) which effectively see only the Euclidean structure of face space, LPP finds an embedding that preserves local information, and obtains a face subspace that best detects the essential face manifold structure. The Laplacianfaces are the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the face manifold. In this way, the unwanted variations resulting from changes in lighting, facial expression, and pose may be eliminated or reduced. Theoretical analysis shows that PCA, LDA, and LPP can be obtained from different graph models. We compare the proposed Laplacianface approach with Eigenface and Fisherface methods on three different face data sets. Experimental results suggest that the proposed Laplacianface approach provides a better representation and achieves lower error rates in face recognition.

[1] A.U. Batur and M.H. Hayes, “Linear Subspace for Illumination Robust Face Recognition,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, Dec. 2001.
[2] P.N. Belhumeur, J.P. Hespanha, and D.J. Kriegman, “Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, July 1997.
[3] M. Belkin and P. Niyogi, “Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering,” Proc. Conf. Advances in Neural Information Processing System 15, 2001.
[4] M. Belkin and P. Niyogi, “Using Manifold Structure for Partially Labeled Classification,” Proc. Conf. Advances in Neural Information Processing System 15, 2002.
[5] M. Brand, “Charting a Manifold,” Proc. Conf. Advances in Neural Information Processing Systems, 2002.
[6] F.R.K. Chung, “Spectral Graph Theory,” Proc. Regional Conf. Series in Math., no. 92, 1997.
[7] Y. Chang, C. Hu, and M. Turk, “Manifold of Facial Expression,” Proc. IEEE Int'l Workshop Analysis and Modeling of Faces and Gestures, Oct. 2003.
[8] R. Gross, J. Shi, and J. Cohn, “Where to Go with Face Recognition,” Proc. Third Workshop Empirical Evaluation Methods in Computer Vision, Dec. 2001.
[9] X. He and P. Niyogi, “Locality Preserving Projections,” Proc. Conf. Advances in Neural Information Processing Systems, 2003.
[10] K.-C. Lee, J. Ho, M.-H. Yang, and D. Kriegman, “Video-Based Face Recognition Using Probabilistic Appearance Manifolds,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 313-320, 2003.
[11] A. Levin and A. Shashua, “Principal Component Analysis over Continuous Subspaces and Intersection of Half-Spaces,” Proc. European Conf. Computer Vision, May 2002.
[12] S.Z. Li, X.W. Hou, H.J. Zhang, and Q.S. Cheng, “Learning Spatially Localized, Parts-Based Representation,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, Dec. 2001.
[13] Q. Liu, R. Huang, H. Lu, and S. Ma, “Face Recognition Using Kernel Based Fisher Discriminant Analysis,” Proc. Fifth Int'l Conf. Automatic Face and Gesture Recognition, May 2002.
[14] A.M. Martinez and A.C. Kak, “PCA versus LDA,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 228-233, Feb. 2001.
[15] B. Moghaddam and A. Pentland, “Probabilistic Visual Learning for Object Representation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, pp. 696-710, 1997.
[16] H. Murase and S.K. Nayar, “Visual Learning and Recognition of 3-D Objects from Appearance,” Int'l J. Computer Vision, vol. 14, pp. 5-24, 1995.
[17] P.J. Phillips, “Support Vector Machines Applied to Face Recognition,” Proc. Conf. Advances in Neural Information Processing Systems 11, pp. 803-809, 1998.
[18] S.T. Roweis and L.K. Saul, “Nonlinear Dimensionality Reduction by Locally Linear Embedding,” Science, vol. 290, Dec. 2000.
[19] S. Roweis, L. Saul, and G. Hinton, “Global Coordination of Local Linear Models,” Proc. Conf. Advances in Neural Information Processing System 14, 2001.
[20] L.K. Saul and S.T. Roweis, “Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifolds,” J. Machine Learning Research, vol. 4, pp. 119-155, 2003.
[21] H.S. Seung and D.D. Lee, “The Manifold Ways of Perception,” Science, vol. 290, Dec. 2000.
[22] T. Shakunaga and K. Shigenari, “Decomposed Eigenface for Face Recognition under Various Lighting Conditions,” IEEE Int'l Conf. Computer Vision and Pattern Recognition, Dec. 2001.
[23] A. Shashua, A. Levin, and S. Avidan, “Manifold Pursuit: A New Approach to Appearance Based Recognition,” Proc. Int'l Conf. Pattern Recognition, Aug. 2002.
[24] J. Shi and J. Malik, “Normalized Cuts and Image Segmentation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, pp. 888-905, 2000.
[25] T. Sim, S. Baker, and M. Bsat, “The CMU Pose, Illumination, and Expression (PIE) Database,” Proc. IEEE Int'l Conf. Automatic Face and Gesture Recognition, May 2002.
[26] L. Sirovich and M. Kirby, “Low-Dimensional Procedure for the Characterization of Human Faces,” J. Optical Soc. Am. A, vol. 4, pp. 519-524, 1987.
[27] J.B. Tenenbaum, V. de Silva, and J.C. Langford, “A Global Geometric Framework for Nonlinear Dimensionality Reduction,” Science, vol. 290, Dec. 2000.
[28] M. Turk and A.P. Pentland, “Face Recognition Using Eigenfaces,” IEEE Conf. Computer Vision and Pattern Recognition, 1991.
[29] L. Wiskott, J.M. Fellous, N. Kruger, and C.v.d. Malsburg, “Face Recognition by Elastic Bunch Graph Matching,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, pp. 775-779, 1997.
[30] R. Xiao, L. Zhu, and H.-J. Zhang, “Boosting Chain Learning for Object Detection,” Proc. IEEE Int'l Conf. Computer Vision, 2003.
[31] Yale Univ. Face Database, http://cvc.yale.edu/projects/yalefacesyalefaces.html , 2002.
[32] S. Yan, M. Li, H.-J. Zhang, and Q. Cheng, “Ranking Prior Likelihood Distributions for Bayesian Shape Localization Framework,” Proc. IEEE Int'l Conf. Computer Vision, 2003.
[33] M.-H. Yang, “Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods,” Proc. Fifth Int'l Conf. Automatic Face and Gesture Recognition, May 2002.
[34] J. Yang, Y. Yu, and W. Kunz, “An Efficient LDA Algorithm for Face Recognition,” Proc. Sixth Int'l Conf. Control, Automation, Robotics and Vision, 2000.
[35] H. Zha and Z. Zhang, “Isometric Embedding and Continuum ISOMAP,” Proc. 20th Int'l Conf. Machine Learning, pp. 864-871, 2003.
[36] Z. Zhang and H. Zha, “Principal Manifolds and Nonlinear Dimension Reduction via Local Tangent Space Alignment,” Technical Report CSE-02-019, CSE, Penn State Univ., 2002.
[37] W. Zhao, R. Chellappa, and P.J. Phillips, “Subspace Linear Discriminant Analysis for Face Recognition,” Technical Report CAR-TR-914, Center for Automation Research, Univ. of Maryland, 1999.

Index Terms:
Face recognition, principal component analysis, linear discriminant analysis, locality preserving projections, face manifold, subspace learning.
Citation:
Xiaofei He, Shuicheng Yan, Yuxiao Hu, Partha Niyogi, Hong-Jiang Zhang, "Face Recognition Using Laplacianfaces," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 3, pp. 328-340, March 2005, doi:10.1109/TPAMI.2005.55
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