Abstract
A novel face recognition method is proposed in this paper to alleviate the "Small Sample Size" problem of the conventional Linear Discriminant Analysis (LDA). This method is based on the feature extraction of global odd and even face image representation, and a dimension reduction process via Symmetrical 2D Partial Least Square Analysis (2DPLS) by two sizes. The low-dimensional features are then used to train a LDA classifier. Experimental results on Yale Face Database B and Feret face Database demonstrate that our framework is highly efficient and gives the state-of-the-art recognition rate.