Bilateral Two-Dimensional Neighborhood Preserving Discriminant Embedding for Face Recognition

Abstract

In this paper, we propose a novel bilateral 2-D neighborhood preserving discriminant embedding for supervised linear dimensionality reduction for face recognition. It directly extracts discriminative face features from images based on graph embedding and Fisher’s criterion. The proposed method is a manifold learning algorithm based on graph embedding criterion, which can effectively discover the underlying nonlinear face data structure. Both within-neighboring and between-neighboring information are taken into account to seek an optimal projection matrix by minimizing the intra-class scatter and maximizing the inter-class scatter based on Fisher’s criterion. The performance of the proposed method is evaluated and compared with other face recognition schemes on the Yale, PICS, AR, and LFW databases. The experiment results demonstrate the effectiveness and superiority of the proposed method as compared with the state-of-the-art dimensionality reduction algorithms.