Symmetry Based Two-Dimensional Principal Component Analysis for Face Recognition

Authors:
Mingyong Ding;Congde Lu;Yunsong Lin;Ling Tong
Affiliations:
School of Computer Science and Information Engineering, Chongqing Technology and Business University, Chongqing 400067, China;School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China;School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China;School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

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Abstract

Two-dimensional principal component analysis (2DPCA) proposed recently overcome a limitation of principal component analysis (PCA) which is expensive computational cost. Symmetrical principal component analysis (SPCA) is also a better feature extraction technique because it utilizes effectively the symmetrical property of human face. This paper presents a symmetry based two-dimensional principal component analysis (S2DPCA), which combines the advantages of 2DPCA and of the SPCA. The experimental results show that S2DPCA is competitive with or superior to 2DPCA and SPCA.