Intra-class Low-Rank Subspace Learning for Face Recognition

Abstract

As a simple and effective tool, linear regression has been widely used in pattern recognition. However, the direct projection from high-dimensional data to binary labels may not be flexible enough and suitable data rep-resentation for classification problems cannot be got. In order to solve this problem, the label relaxation method has been proposed. Although its effectiveness has been proven, the problems that it will increase the difference between targets from same class still exist. Therefore, an intra-class low-rank subspace learning (ICLRSL) method is pro-posed in this paper, which is different from the original linear regression and the label relaxation based method. Double projection matrices are used to perform intra-class low-rank subspace projection and label space projection respectively. The intra-class low-rank subspace obtained by ICLRSL is used as a bridge between the high-dimensional data space and the label space, and the preliminary coding of the data can be obtained, which has similar intra-class correlation with the final regression targets through intra-class low-rank constraint. At the same time, the row sparsity constraint ensures that the subspace projection focuses on the few features most relevant to the intra-class low-rank property, and reduces the negative impact of redundant information to some extent. Through the con-nection of intermediate subspace, on the one hand, it has more flexibility than directly learning a single projection matrix, and on the other hand, it can also obtain discriminative data representation. Experimental results on four public face datasets demonstrate the effectiveness of the ICLRSL algorithm.

Keywords

• |face recognition|supervised learning|subspace projection|intra-class low-rank subspace