Matrix Based Feature Measurement and Extraction for Face Recognition
2015 ◽
Vol 738-739
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pp. 643-647
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In this paper, a matrix based feature extraction and measurement method, i.e.: multi-column principle component analysis (MCPCA) is used to directly and effectively extract features from the matrix. We analyze the advantages of MCPCA over the conventional principal component analysis (PCA) and two-dimensional PCA (2DPCA), and we have successfully applied it into face image recognition. Extensive face recognition experiments illustrate that the proposed method obtains high accuracy, and it is more robust than previous conventional face recognition methods.
2013 ◽
pp. 138-145
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2015 ◽
Vol 32
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pp. 55-62
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2017 ◽
Vol 2017
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pp. 1-9
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