Face Recognition Using Marginal Discriminant Linear Local Tangent Space Alignment

In this paper, discriminant linear local tangent space alignment (MDLLTSA) is proposed in order to solve the problems of local tangent space alignment (LTSA) in image recognition, such as implicitness of the nonlinear map or class information of data is ignored. With local tangent space representing for local geometrical structure of the manifold of the data samples, as well as with the supervised information for minimizing the margin of the intraclass and maximizing the margin of interclass, we convert the optimization problem of LTSA into multi-object optimization problem to obtain feature extraction space. Compared with classical feature extraction methods, the proposed algorithm obtained stronger classification performance and preserved local geometrical structure as well.