A Reshaped 2DPCA Algorithm Based on the Vertical Symmetry of Face

J4 ›› 2011, Vol. 33 ›› Issue (7): 74-79.

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A Reshaped 2DPCA Algorithm Based on the Vertical Symmetry of Face

ZENG Yue1,2,FENG Dazheng1

(1.The State Key Laboratory of Radar Signal Processing,Xidian University,Xi’an 710071;
2.Department of Information Engineering,
Jiangxi Vocational College of Finance and Economics,Jiujiang 332000,China)

Received:2010-08-30 Revised:2010-12-24 Online:2011-07-21 Published:2011-07-25

Abstract

Abstract:

this paper the vertical symmetry of face, the characteristics of PCA and 2DPCA are discussed. And it is proved that the covariance matrix of 2DPCA is equivalent to the average of the main diagonal of the PCA covariance matrix, and eliminates the covariance information that can be useful for recognition. A reshaped 2DPCA algorithm based on the vertical symmetry of face (S2DPCA) is proposed which can make the most useful of the covariance discriminate information, represents a face with fewer coefficients. The experiments on the ORL face bases show it reduces the computational complexity compared with PCA, improve the recognition rate of face compared with PCA and 2DPCA, and is also superior to the traditional algorithms (ICA, eigenfaces and Keinel eigenfaces), and shows a face image with fewer coefficients.

Key words: PCA(principal component analysis);twodimensional PCA(2DPCA);face recognition;face representation

ZENG Yue1,2,FENG Dazheng1. A Reshaped 2DPCA Algorithm Based on the Vertical Symmetry of Face[J]. J4, 2011, 33(7): 74-79.

References

［1］Turk M, Pentland A. Eigenfaces for Recognition［J］.Journal of Cognitive Neuroscience, 1991, 3(1):7186.
［2］Yang J, Zhang D, Frangi Alejandro F, et al. TwoDimensional PCA: A New Approach to AppearanceBased Face Representation and Recognition［J］. Journal of IEEE Transaction on Pattern Analysis and Machine Intelligence, 2004, 26(1):131137.
［3］Visani M, Garcia C, Laurent C. Comparing Robustness of TwoDimensional PCA and Eigenfaces Recognition［J］. Lecture Notes Computer Science, 2004, 32(12):717724.
［4］Zuo W M, Zhang D, Wang K. An Assembled Matrix Distance Metric for 2DPCABased Image Recognition［J］. Pattern Recognition Letters, 2006, 27(3):210216.
［5］Zhang D Q, Zhou Z H. (2D)2PCA:TwoDirectional TwoDimensional PCA for Efficient Face Representation and Recognition［J］. Neurocomputing, 2005, 69（13):224231.
［6］Ye J. Generalized Low Rank Approximations of Matrices［J］. Machine Learning, 2005, 61(13):167191.
［7］Wang L W, Wang X, Feng J F. On Image Matrix based Feature Extraction Algorithms［J］. IEEE Trans on Systems, Man, Cybernetics B, 2006,36(1):194197.
［8］Kong H, Wang L, Teoh E K, et al. Generalized 2D Principal Component Analysis for Face Image Representation and Recognition［J］. Neural Networks, 2005, 18(56):585594.
［9］Zheng W S, Lai J H, Li S Z. 1DLDA vs. 2DLDA: When is VectorBased Linear Discriminant Analysis Better than MatrixBased?［J］. Pattern Recognition, 2008, 41(7):21562172.
［10］Turk M, Pentland A. Face Recognition Using Eigenfaces［C］∥Proc of IEEE Conf on Computer Vision and Pattern Recognition, 1991:586591.
［11］Yang J, Yang J Y. From Image Vector to Matrix: A Straightforward Image Projection Technique—IMPCA vs. PCA ［J］. Pattern Recognition, 2002, 35(9):19971999.
［12］Zhao L,Yang Y. Theoretical Analysis of Illumination in PCABased Vision System［J］. Pattern Recognition, 1999, 32(4):547564.
［13］Belhumeur P N, Hespanha J P, Kriengman D J. Eigenfacs vs Fisherfaces: Recognition Using Class Specific Linear Projection［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997,19(7):711720.
［14］Yuen P C,Lai J H. Face Representation Using Independent Component Analysis［J］. Pattern Recognition, 2002, 35(6):12471257.
［15］Yang M H. Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods［C］∥Proc of the Fifth IEEE Int’l Conf on Automatic Face and Gesture Recognition, 2002:21522.

A Reshaped 2DPCA Algorithm Based on the Vertical Symmetry of Face

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