Abstract
In this paper, we combine the advantages of (2D) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> PCA and MMC, and propose a two-stage framework: “(2D) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> PCA+ MMC”. Since the extracted features based on (2D) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> PCA are most expressive and based on maximal margin criterion (MMC) are robust, stable and efficient, in the first stage, a 2D two-directional feature extraction technique, (2D) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> PCA, is employed to condense the dimension of image matrix; in the second stage, the linear discriminant analysis (MMC) is performed in the (2D) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> PCA subspace to find the optimal discriminant feature vectors. In addition, the proposed method can make use of the descriptive information and discriminant information of the image. Experiments conducted on ORL and Yale face databases demonstrate the effectiveness and robustness of the proposed method.
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