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Volume 33 Issue 4
May  2011
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Zeng Yue, Feng Da-Zheng. An Algorithm of Feature Extraction of Face Based on the Weighted Variation of 2DPCA[J]. Journal of Electronics & Information Technology, 2011, 33(4): 769-774. doi: 10.3724/SP.J.1146.2010.01003
Citation: Zeng Yue, Feng Da-Zheng. An Algorithm of Feature Extraction of Face Based on the Weighted Variation of 2DPCA[J]. Journal of Electronics & Information Technology, 2011, 33(4): 769-774. doi: 10.3724/SP.J.1146.2010.01003

An Algorithm of Feature Extraction of Face Based on the Weighted Variation of 2DPCA

doi: 10.3724/SP.J.1146.2010.01003
  • Received Date: 2010-09-14
  • Rev Recd Date: 2010-11-29
  • Publish Date: 2011-04-19
  • This paper first discusses the relationship of Principal Component Analysis (PCA) and 2-Dimensional PCA (2DPCA). For 2DPCA eliminating the some covariance information which can be useful for recognition, and PCAs small sample size problem, an algorithm of feature extraction of face based on the Weighted Variation 2DPCA (WV2DPCA) is proposed. Three sub-parts of the face features are extracted respectively in the method of the variation of 2DPCA, and then are classified according to the weight and the Nearest neighbor theory. The experiments on both of ORL and YALE face bases show improvement in recognition accuracy, fewer coefficients and recognition time over 2DPCA, and this algorithm is also superior to the traditional eigenfaces, ICA and Kernel Eigenfaces in terms of the recognition accuracy.
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  • Yang J, Zhang D, and Alejandro F, et al.. Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2004, 26(1): 131-137.[2] Rajkiran G and Asari V K. An improved face recognition technique based on modular PCA approach. Pattern Recognition Letters, 2004, 25(4): 429-436.[3] Zuo Wang-meng, Zhang D, and Wang Kuan-quan.Bidirectional PCA with assembled matrix distance Metric for image recognition. IEEE Transaction on Systems, Man, and Cybernetics-part B: Cyebrnetics, 2006, 36(4): 863-872.[4] Chen Song-can and Zhu Yu-lian. Subpattern-based principle component analysis. Pattern Recognition, 2004, 37(5): 1081-1083.[5] Tan Ke-ren and Chen Song-can. Adaptively weighted sub-pattern PCA for face recognition. Neurocomputing, 2005, 64(1): 505-511.[6] Eftekhari A. Mohamad Forouzanfar and Hamid Abrishami Moghaddam. Block-wised 2D kernel PCA/LDA for recognition. Information Processing Letters, 2010, 110(17): 761-766.[7] Huang Guo-hong. Fusion (2D)2PCALDA: a new method for face recognition. Applied Mathematics and Computation, 2010, 216(11): 3195-3199.[8] Qi Yong-feng and Zhang Jia-shu. (2D)2PCALDA: an efficient approach for face recognition. Applied Mathematics and Computation, 2009, 213(1): 1-7.[9] Wang Jin, Barret A, and Wang Lu, et al.. Multilinear principal component analysis for face recognition with fewer features. Neurocomputing, 2010, 73(10-12): 1550-1555.[10] Yu W, Wang Z, and Chen W. A new framework to combine vertical and horizontal information for face recognition. Neurocomputing, 2009, 72(4-6): 1084-1091.[11] Zheng Wei-shi, Lai J H, and Li S Z. 1D-LDA vs. 2DLDA: when is vector-based linear discriminant analysis better than matrix-based?. Pattern Recognition, 2008, 41(7): 2156-2172.[12] Belhumeur P N, Hespanha J P, and Kriengman D J. Eigenfacs vs. fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720.[13] Yuen P C and Lai J H. Face representation using independent component analysis. Pattern Recognition, 2002, 35(6): 1247-1257.[14] Yang M H. Kernel eigenfaces vs. kernel fisherfaces: face recognition using kernel methods. Proc Fifth IEEE int1 conf. Automatic Face and Gesture Recognition(RGR02), Washinton D C, May 2002: 215-220.
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