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Bilateral Two-Dimensional Principal Component Analysis with its Application to Face Recognition

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5 Author(s)
Xiaoguo Wang ; Inst. of Commun. Eng., PLA Univ. of Sci. & Tech., Nanjing ; Baoming Liu ; Xiongwei Zhang ; Jun Liu
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In this paper, we propose a novel algorithm for face feature extraction, namely the bilateral two-dimensional principal component analysis (B2DPCA), which directly extracts the proper features from image matrices. As opposed to PCA, 2DPCA is based on 2D image matrices rather than 1D vector so the image matrix does not need to be transformed into a vector prior to feature extraction. Experiments on ORL and Yale face database are performed to test and evaluate the proposed algorithm. The results demonstrate the effectiveness of proposed algorithm.

Published in:

Intelligent Systems and Applications, 2009. ISA 2009. International Workshop on

Date of Conference:

23-24 May 2009