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Volume 26 Issue 2
Feb.  2004
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Sun Da-rui, Wu Le-nan. Face Recognition Based on Nonlinear Feature Extraction and SVM[J]. Journal of Electronics & Information Technology, 2004, 26(2): 307-311.
Citation: Sun Da-rui, Wu Le-nan. Face Recognition Based on Nonlinear Feature Extraction and SVM[J]. Journal of Electronics & Information Technology, 2004, 26(2): 307-311.

Face Recognition Based on Nonlinear Feature Extraction and SVM

  • Received Date: 2002-10-07
  • Rev Recd Date: 2003-02-28
  • Publish Date: 2004-02-19
  • Both PCA and LDA are performed by only using the second-order statistics among image pixels, and not sensitive to high order statistics in the data. In this paper, the kernel function method is used to extract the high order relations, and the Linear Support Vector Machines (LSVM) is selected to perform the face classification. The experiment on Yale face database shows that the nonlinear feature extraction method is effective, and SVM is better than nearest neighbor classifier.
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  • Scholkopf B, Smola A, Muller K. Nonlinear conponent analysis as a kernel eigenvalue problem[J].Neural Computation.1998, 10(5):1299-1319[2]Baudat G, Anouar F. Generalized discriminant analysis using a kernel approach[J].Neural Computation.2000, 12(10):2385-2404[3]Kim K I, Jung K, Kim H J. Face recognition using kernel principal component analysis[J].IEEE Signal Processing Letters.2002, 9(2):40-42[4]Moghaddam B. Principal manifolds and probabilistic subspaces for visual recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2002, PAMI-24(6): 780-788.[5]Yang MH. Kernel eigenfaces vs. kernel fisherfaces: face recognition using kernel methods. Proc.of 5th IEEE Int. Conf. on Autonatic Face and Gesture Recognition, Washington D. C., 2002:215-220.[6]Vapnik V. The Nature of Statistical Learning Theory, New York, NY: Wiley, 1998, Chapter 5.[7]Guo G, Li S Z, Chan K L. Support vector machine for face recognition[J].Image and Vision Computing.2001, 19(9-10):631-638[8]Mika S, Ratsch G, et al.. Fisher discriminant analysis with kernels. In Y. H. Hu, J. Larsen, E.Wilson, S. Douglas, ed., Neural Networks for Signal Processing, IEEE, 1999, IX: 41-48.[9]Scholkopf B, Smola A, et al.. New support vector algorithms[J].Neural Computation.2000, 12(5):1207-1245
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