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8 July 2011 (2D)2PCA+(2D)2LDA: a new feature extraction for face recognition
Guohong Huang
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Proceedings Volume 8009, Third International Conference on Digital Image Processing (ICDIP 2011); 800934 (2011) https://doi.org/10.1117/12.896278
Event: 3rd International Conference on Digital Image Processing, 2011, Chengdu, China
Abstract
In this paper, we combine the advantages of (2D)2PCA and (2D)2LDA, and propose a two-stage framework: "(2D)2PCA+(2D)2LDA". In the first stage, a two-directional 2D feature extraction technique, (2D)2PCA, is employed to condense the dimension of image matrix; in the second stage, the two-directional 2D linear discriminant analysis (2D)2LDA is performed in the (2D)2PCA subspace to find the optimal discriminant feature vectors. In addition, the proposed method can take full advantage 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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Guohong Huang "(2D)2PCA+(2D)2LDA: a new feature extraction for face recognition", Proc. SPIE 8009, Third International Conference on Digital Image Processing (ICDIP 2011), 800934 (8 July 2011); https://doi.org/10.1117/12.896278
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KEYWORDS
Feature extraction
Databases
Facial recognition systems
Principal component analysis
Matrices
Detection and tracking algorithms
Light sources and illumination
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