Two-Dimensional Discriminant Canonical Correlation  Analysis for Face Recognition

 We present a face recognition method called two-dimensional discriminant canonical correlation analysis (2D-DCCA) which is based on Canonical Correlation Analysis (CCA). The main idea is that the concept of two order tensor is combined with CCA in this paper. A sample is usually represented as a vector in the conventional CCA method which consumes lots of memory and has the singular problem. The proposed method not only makes full use of the information of within-class and between-class, but also the samples here are represented as the matrices. Hence the proposed method has these advantages: low dimensional subspace, efficient computation and the singular problem is totally avoided. The objective functions are optimized by using the information of within-class and between-class, so the accuracy of face recognition improves in the nearest neighborhood classifier. The result of the following experiments shows that the proposed method is robust when the pose of the face varies.