Sparse modified marginal fisher analysis for facial expression recognition

Abstract

Marginal Fisher analysis (MFA) is an efficient method for dimension reduction, which can extract useful discriminant features for image recognition. Since sparse learning can achieve better generalization ability and lessen the amount of computations in recognition tasks, this paper introduces sparsity into MFA and proposes a novel sparse modified MFA (SMMFA) method for facial expression recognition. The goal of SMMFA is to extract discriminative features by using the resulted sparse projection matrix. First, a modified MFA is proposed to find the original projection matrix. Similar to MFA, the modified MFA also defines the intra-class graph and the inter-class graph to describe geometry structure in the same class and local discriminant structure between different classes, respectively. In addition, the modified MFA removes the null space of the total scatter matrix. The sparse solution of SMMFA can be gained by solving the l1 –minimization problem on the original projection matrix using the linearized Bregman iteration. Experimental results show that the proposed SMMFA can effectively extract intrinsic features and has better discriminant power than the state-of-the-art methods.