A New LDA-based Face Recognition System Which Can Solve the Small Sample Size Problem

A new LDA-based face recognition system is presented in this paper. Linear discriminant analysis (LDA) is one of the most popular linear projection techniques for feature, extraction. The major drawback of applying LDA is that it may encounter the small sample size problem. This problem arises whenever the number of samples is smaller than the dimensionality of samples. Under the circumstances, the sample scatter matrix may become singular and the execution of LDA may have computational difficulty. In this paper, we propose a new LDA-based technique which can solve the small sample size problem. We also prove that the most expressive vectors derived in the null space of the within-class scatter matrix using Principal Component Analysis (PCA) are equal to the optimal discriminant vectors derived in the original space using LDA. The experimental results showed that the new LDA process improves the performance of a face recognition system significantly.