Local feature based multi-view discriminant analysis

In many real-world applications, an object can be represented from multiple views or styles. Thus, it is important to design algorithms that are able to recognize objects from distinct views. To the end, a large number of approaches have been proposed to achieve the heterogeneous recognition tasks through the use of local features. However, most of them only focus on binary views and thus cannot be applied to multi-view analysis. In this paper, we propose a novel local feature based multi-view discriminant analysis approach (FMDA). The proposed approach consists of three steps: First, the input images are represented using representation matrices and local feature descriptor (LFD) matrices of their overlapping patches, where the representation matrices are the linear coefficients of the LFDs for different views. In this way, it brings two advantages, i.e., addressing the small sample size (SSS) problem and preserving the discriminative information while reducing the redundant information in the LFD matrices. Second, the multi-view discriminant representation and feature projections are learned by projecting the LFDs of different views into a common space using the Fisher criterion. Finally, a simple but effective view-similarity constraint is proposed to adaptively learn the relationships between different views. To verify the effectiveness of the proposed method, extensive experiments are carried out on the FERET, CAS-PEAL-R1, CUFSF and HFB databases comparing with some state-of-the-art methods.