A multi-manifold discriminant analysis method for image feature extraction

Authors:
Wankou Yang;Changyin Sun;Lei Zhang
Affiliations:
School of Automation, Southeast University, Nanjing 210096, China;School of Automation, Southeast University, Nanjing 210096, China;Biometrics Research Centre, Department of Computing, the Hong Kong Polytechnic University, Hong Kong
Venue:
Pattern Recognition
Year:
2011

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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.