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Issue No.04 - April (2009 vol.31)
pp: 755-761
Zhifeng Li , The Chinese University of Hong Kong, Hong Kong
Dahua Lin , Massachusetts Institute of Technology, Cambridge
Xiaoou Tang , The Chinese University of Hong Kong, Hong Kong
ABSTRACT
In this paper, we develop a new framework for face recognition based on nonparametric discriminant analysis (NDA) and multi-classifier integration. Traditional LDA-based methods suffer a fundamental limitation originating from the parametric nature of scatter matrices, which are based on the Gaussian distribution assumption. The performance of these methods notably degrades when the actual distribution is Non-Gaussian. To address this problem, we propose a new formulation of scatter matrices to extend the two-class nonparametric discriminant analysis to multi-class cases. Then, we develop two more improved multi-class NDA-based algorithms (NSA and NFA) with each one having two complementary methods based on the principal space and the null space of the intra-class scatter matrix respectively. Comparing to the NSA, the NFA is more effective in the utilization of the classification boundary information. In order to exploit the complementary nature of the two kinds of NFA (PNFA and NNFA), we finally develop a dual NFA-based multi-classifier fusion framework by employing the over complete Gabor representation to boost the recognition performance. We show the improvements of the developed new algorithms over the traditional subspace methods through comparative experiments on two challenging face databases, Purdue AR database and XM2VTS database.
INDEX TERMS
Face and gesture recognition, Classifier design and evaluation
CITATION
Zhifeng Li, Dahua Lin, Xiaoou Tang, "Nonparametric Discriminant Analysis for Face Recognition", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.31, no. 4, pp. 755-761, April 2009, doi:10.1109/TPAMI.2008.174
REFERENCES
[1] P. Belhumeur, J. Hespanda, and D. Kiregeman, “Eigenfaces versus Fisherfaces: Recognition Using Class Specific Linear Projection,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, July 1997.
[2] H. Chen, H. Chang, and T. Liu, “Local Discriminant Embedding and Its Variants,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2005.
[3] L. Chen, H. Liao, J. Lin, M. Ko, and G. Yu, “A New LDA-Based Face Recognition System Which Can Solve the Small Sample Size Problem,” Pattern Recognition, vol. 33, no. 10, 2000.
[4] K. Etemad and R. Chellappa, “Face Recognition Using Discriminant Eigenvectors,” Proc. IEEE Int'l Conf. Acoustics, Speech, and Signal Processing, vol. 4, pp. 2148-2151, 1996.
[5] K. Etemad and R. Chellappa, “Discriminant Analysis for Recognition of Human Face Images,” J. Optical Soc. Am. A, vol. 14, no. 8, pp. 1724-1733, 1997.
[6] R. Fisher, “The Use of Multiple Measures in Taxonomic Problems,” Annals of Eugenics, vol. 7, pp. 179-188, 1936.
[7] K. Fukunaga, Statistical Pattern Recognition. Academic Press, 1990.
[8] T.K. Ho, J. Hull, and S. Srihari, “Decision Combination in Multiple Classifier Systems,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 16, no. 1, pp. 66-75, Jan. 1994.
[9] Z. Li, W. Liu, D. Lin, and X. Tang, “Nonparametric Subspace Analysis for Face Recognition,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2005.
[10] C. Liu and H. Wechsler, “Gabor Feature Based Classification Using the Enhanced Fisher Linear Discriminant Model for Face Recognition,” IEEE Trans. Image Processing, vol. 11, no. 4, pp. 467-476, 2002.
[11] M. Loog and R.P.W. Duin, “Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 6, pp. 732-739, June 2004.
[12] A.M. Martinez and R. Benavente, “The AR Face Database,” CVC Technical Report 24, Purdue Univ., June 1998.
[13] K. Messer, J. Matas, J. Kittler, J. Luettin, and G. Matitre, “XM2VTSDB: The Extended M2VTS Database,” Proc. Second Int'l Conf. Audio- and Video-Based Biometric Person Authentication, Mar. 1999.
[14] B. Moghaddam, T. Jebara, and A. Pentland, “Bayesian Face Recognition,” Pattern Recognition, vol. 33, pp. 1771-1782, 2000.
[15] D.L. Swets and J. Weng, “Using Discriminant Eigenfeatures for Image Retrieval,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 8, pp. 831-836, Aug. 1996.
[16] X. Tang and Z. Li, “Frame Synchronization and Multi-Level Subspace Analysis for Video Based Face Recognition,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2004.
[17] M. Turk and A. Pentland, “Face Recognition Using Eigenfaces,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, pp. 586-591, 1991.
[18] X. Wang and X. Tang, “A Unified Framework for Subspace Face Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 9, pp. 1222-1228, Sept. 2004.
[19] X. Wang and X. Tang, “Dual-Space Linear Discriminant Analysis for Face Recognition,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2004.
[20] X. Wang and X. Tang, “Random Sampling LDA for Face Recognition,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2004.
[21] X. Wang and X. Tang, “Random Sampling for Subspace Face Recognition,” Int'l J. Computer Vision, vol. 70, no. 1, pp. 91-104, 2006.
[22] L. Wiskott, J.M. Fellous, N. Krüger, and C. von der Malsburg, “Face Recognition by Elastic Bunch Graph Matching,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 775-779, July 1997.
[23] L. Xu, A. Krzyzak, and C.Y. Suen, “Method of Combining Multiple Classifiers and Their Applications to Handwriting Recognition,” IEEE Trans. Systems, Man, and Cybernetics, vol. 22, no. 3, pp. 418-435, 1992.
[24] M. Yang, “Kernel Eigenface versus Kernel Fisherface: Face Recognition Using Kernel Methods,” Proc. Fifth Int'l Conf. Automatic Face and Gesture Recognition, 2002.
[25] W. Zhao and R. Chellappa, “Discriminant Analysis of Principal Components for Face Recognition,” Proc. Third IEEE Conf. Automatic Face and Gesture Recognition, pp. 336-341, 1998.
[26] W. Zhao, “Discriminant Component Analysis for Face Recognition,” Proc. Int'l Conf. Pattern Recognition, vol. 2, pp. 818-821, 2000.
[27] W. Zhao, R. Chellappa, and N. Nandhakumar, “Empirical Performance Analysis of Linear Discriminant Classifiers,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 164-169, 1998.
[28] W. Zheng, C. Zou, and L. Zhao, “Real-Time Face Recognition Using Gram-Schmidt Orthogonalization for LDA,” Proc. IEEE Conf. Pattern Recognition, pp. 403-406, 2004.
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