Two-Dimensional Local Graph Embedding Analysis(2DLGEA) for Face Recognition

Abstract

This paper proposes a novel method, called two-dimensional local graph embedding analysis (2DLGEA), for image feature extraction, which can directly extracts the optimal projective vectors from 2D image matrices rather than image vectors based on the scatter difference criterion. In graph embedding, the intrinsic graph characterizes the intraclass compactness and connects each data point with its neighboring within the same class, while the penalty graph connects the marginal points and characterizes the interclass separability. The proposed method effectively avoids the singularity problem frequently occurred in the classical linear discriminant analysis due to the small sample size and overcomes the limitations of the traditional linear discriminant analysis algorithm due to data distribution assumptions and available projection directions. Experimental results on Yale, and ORL face databases show the effectiveness of the proposed method.