A New Face Recognition Framework: Symmetrical Bilateral 2DPLS plus LDA

A novel face recognition framework is proposed in this paper to alleviate "Small Sample Size" (SSS) 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 Bilateral 2D Partial Least Square Analysis(2DPLS). The low-dimensional features are then used to train a LDA classifier which uses Frobenius-norm classification measure, and uses pseudo inverse to make sure between-class matrix Sw be full rank. Experimental results on Yale Face Database B, ORL, and FERET Face Database demonstrate that our framework is highly efficient and gives the state-of-the-art recognition rate.