Block LDA for Face Recognition

Linear Discriminant Analysis (LDA) technique is an important and well-developed area of image recognition and to date many linear discrimination methods have been put forward. Despite these efforts, there persist in the traditional LDA some weaknesses. In this paper, we propose a new LDA-based method called Block LDA (BLDA) that can outperform the traditional Linear Dicriminant Analysis (LDA) methods. As opposed to conventional LDA, BLDA is based on 2D matrices rather than 1D vectors. That is, we firstly divides the original image into blocks. Then, we transform the image into a vector of blocks. By using row vector to represent each block, we can get the new matrix which is the representation of the image. Finally LDA can be applied directly on these matrices. In contrast to the between-class and within-class covariance matrices of LDA, the size of the these covariance matrices using BLDA is much smaller. As a result, BLDA has three important advantages over LDA. First, it is easier t...

• Type: Conference
• Year: 2005
• Where: IWANN
• Authors: Vo Dinh Minh Nhat, Sungyoung Lee
• Keywords: Covariance Matrices, IWANN 2005, Traditional Lda, Within-class Covariance Matrices