Algorithm study of face recognition on improved 2DLDA

Linear Discriminant Analysis (LDA) ([1]) is a well-known method for face recognition in feature extraction and dimension reduction. To solve the "small sample" effect of LDA, Two-Dimensional Linear Discriminant Analysis (2DLDA) ([2]) has been used for face recognition recently, but its could hardly take use of the relationship between the adjacent scatter matrix. In this paper, I improved the between-class scatter matrix, proposed paired-class scatter matrix for face representation and recognition. In this new method, a paired between-class scatter matrix distance metric is used to measure the distance between random paired between-class scatter matrix. To test this new method, ORL face database is used and the results show that the paired between-class scatter matrix based 2DLDA method (N2DLDA) outperforms the 2DLDA method and achieves higher classification accuracy than the 2DLDA algorithm.