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- W2034288102 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." @default.
- W2034288102 created "2016-06-24" @default.
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- W2034288102 date "2010-01-01" @default.
- W2034288102 modified "2023-09-26" @default.
- W2034288102 title "(2D)<sup>2</sup>PCA plus MMC: A new feature extraction for face recognition" @default.
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- W2034288102 doi "https://doi.org/10.1109/icime.2010.5477972" @default.
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