Efficient pseudo-inverse linear discriminant analysis and its nonlinear form for face recognition

Abstract. Pseudoinverse Linear Discriminant Analysis (PLDA) is a classical and pioneer method that deals with the Small Sample Size (SSS) problem in LDA when applied to such application as face recognition. However, it is expensive in computation and storage due to manipulating on extremely large d × d matrices, where d is the dimensionality of the sample image. As a result, although frequently cited in literature, PLDA is hardly compared in terms of classification performance with the newly proposed methods. In this paper, we propose a new feature extraction method named RSw+LDA, which is 1) much more efficient than PLDA in both computation and storage; and 2) theoretically equivalent to PLDA, meaning that it produces the same projection matrix as PLDA. Our experimental results on AR face dataset, a challenging dataset with variations in expression, lighting and occlusion, show that PLDA (or RSw+LDA) can achieve significantly higher classification accuracy than the recently proposed Linear Discriminant Analysis via QR decomposition and Discriminant Common Vectors