Regional Covariance Matrix-Based Two-Dimensional PCA for Face Recognition

Abstract

Two-dimensional principal component analysis (2DPCA) is widely used in many applications, especially, face recognition. A key factor to improve the performance of the 2DPCA method comes from the efficiency of the covariance matrix. This paper believes that the effective eigenvector can be extracted when the effective covariance matrix is given. Therefore, the computing covariance matrix is a focus point in this paper. The set of the covariance matrix in the 2DPCA and its extensions is usually represented with a single directional correlation, which is then used to obtain a mean covariance matrix by using the average technique. This causes in obtaining the ineffective eigenvector since the covariance matrix is ineffective. In order to obtain the effective eigenvector, a regional covariance matrix-based on 2DPCA method (RCM-2DPCA) is proposed here. The contribution of this paper consists of two main parts including (i) regional matrix calculation for computing the two directional correlations and (ii) ELSSP conversion for extracting the effective representation of the covariance matrix. The experimental results show that the performance of the proposed method is higher than the baseline methods including 2DPCA, I-2DPCA, Bi2DPCA, 2D2PCA and ILM-2DPCA methods on a basis of three well- known datasets-ORL Face, Yale Face, and Yale Face extended B+ datasets.