ISNN 2007: Advances in Neural Networks – ISNN 2007 pp 1048-1055 | Cite as
Symmetry Based Two-Dimensional Principal Component Analysis for Face Recognition
Conference paper
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.
Keywords
Face Recognition Kernel Principal Component Analysis Eigenvalue Decomposition Symmetrical Component Robust Principal Component Analysis
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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