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A Novel Face Feature Extraction Method Based on Two-dimensional Principal Component Analysis and Kernel Discriminant Analysis

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6 Author(s)
Xiaoguo Wang ; Inst. of Commun. Eng., PLA Univ. of Sci. & Technol., Nanjing ; Jun Liu ; Ming Tian ; Yong Huang
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A novel face feature extraction method based on Bilateral Two-dimensional Principal Component Analysis (B2DPCA) and Kernel Discriminant Analysis (KDA) was presented in this paper. In this method, B2DPCA method directly extracts the proper features from image matrices at first, then the KDA was performed on the features to enhance discriminant power. As opposed to PCA, B2DPCA is based on 2D image matrices rather than ID 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:

Information Management and Engineering, 2009. ICIME '09. International Conference on

Date of Conference:

3-5 April 2009