Improving sparse representation-based image classification using truncated total least squares

Abstract

In the field of face recognition, the Sparse Representation-based Classification (SRC) method can effectively deal with many common problems such as face occlusion, lighting and expression changes. Truncated Total Least Squares (TTLS) method is determined by using an optimal cutoff factor k. The choice of truncation factor regularization parameters directly affects the quality of the solution. The truncation of total least squares is suitable for solving the linear problems of the same kind. According to this motivation, a classification method TSRC based on the truncation representation is proposed. The truncated global least squares regularization is fused with sparse representation to optimize the representation coefficients. We performed a large number of experiments with this method on several popular benchmark datasets, and the results showed that the sparse representation can be improved if it is combined with the truncation. In most cases, the proposed truncation-based classification method has higher classification accuracy.