A sparse neighborhood preserving non-negative tensor factorization algorithm for facial expression recognition

Abstract

In this paper, a novel sparse neighborhood preserving non-negative tensor factorization (SNPNTF) algorithm is proposed for facial expression recognition. It is derived from non-negative tensor factorization (NTF), and it works in the rank-one tensor space. A sparse constraint is adopted into the objective function, which takes the optimization step in the direction of the negative gradient, and then projects onto the sparse constrained space. To consider the spatial neighborhood structure and the class-based discriminant information, a neighborhood preserving constraint is adopted based on the manifold learning and graph preserving theory. The Laplacian graph which encodes the spatial information in the face samples and the penalty graph which considers the pre-defined class information are considered in this constraint. By using it, the obtained parts-based representations of SNPNTF vary smoothly along the geodesics of the data manifold and they are more discriminant for recognition. SNPNTF is a quadratic convex function in the tensor space, and it could converge to the optimal solution. The gradient descent method is used for the optimization of SNPNTF to ensure the convergence property. Experiments are conducted on the JAFFE database, the Cohn---Kanade database and the AR database. The results demonstrate that SNPNTF provides effective facial representations and achieves better recognition performance, compared with non-negative matrix factorization, NTF and some variant algorithms. Also, the convergence property of SNPNTF is well guaranteed.