Line-Based PCA and LDA Approaches for Face Recognition

Abstract

AbstractPrincipal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) techniques are 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 PCA and LDA some weaknesses. In this paper, we propose a new Line-based methodes called Line-based PCA and Line-based LDA that can outperform the traditional PCA and LDA methods. As opposed to conventional PCA and LDA, those new approaches are based on 2D matrices rather than 1D vectors. That is, we firstly divide 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 PCA and LDA can be applied directly on these matrices. In contrast to the covariance matrices of traditional PCA and LDA approaches, the size of the image covariance matrices using new approaches are much smaller. As a result, those new approaches have three important advantages over traditional ones. First, it is easier to evaluate the covariance matrix accurately. Second, less time is required to determine the corresponding eigenvectors. And finally, block size could be changed to get the best results. Experiment results show our method achieves better performance in comparison with the other methods.KeywordsPrincipal Component AnalysisFace RecognitionLinear Discriminant AnalysisTraining Sample SizeSmall Sample Size ProblemThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.