A multi-manifold discriminant analysis method for image feature extraction |
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Authors: | Wankou Yang [Author Vitae] [Author Vitae] Lei Zhang [Author Vitae] |
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Affiliation: | a School of Automation, Southeast University, Nanjing 210096, China b Biometrics Research Centre, Department of Computing, the Hong Kong Polytechnic University, Hong Kong |
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Abstract: | In this paper, we propose a Multi-Manifold Discriminant Analysis (MMDA) method for an image feature extraction and pattern recognition based on graph embedded learning and under the Fisher discriminant analysis framework. In an MMDA, the within-class graph and between-class graph are, respectively, designed to characterize the within-class compactness and the between-class separability, seeking for the discriminant matrix to simultaneously maximize the between-class scatter and minimize the within-class scatter. In addition, in an MMDA, the within-class graph can represent the sub-manifold information, while the between-class graph can represent the multi-manifold information. The proposed MMDA is extensively examined by using the FERET, AR and ORL face databases, and the PolyU finger-knuckle-print databases. The experimental results demonstrate that an MMDA is effective in feature extraction, leading to promising image recognition performance. |
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Keywords: | Multi-manifold learning LDA Feature extraction Image recognition |
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