A multi-manifold discriminant analysis method for image feature extraction

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.

Research highlights
► We proposed a multi-manifold discriminant analysis (MMDA) method for the feature extraction.
► In an MMDA, two graphs are constructed to model multi-manifolds for classification.
► An MMDA is based on graph embedded learning and is under the Fisher discriminant analysis framework.
► An MMDA is evaluated on three benchmark face databases and the PolyU FKP database.