In this paper, we propose a graph maximum margin criterion (GMMC) which provides a unified method for overcoming the small sample size problem encountered by the algorithms interpreted in a general graph embedding framework. The proposed GMMC-based feature extraction algorithms compute the discriminant vectors by maximizing the difference between the graph between-class scatter matrix and the graph within-class scatter matrix and then the singularity problem is avoided. An efficient and stable algorithm for implementing GMMC is also proposed. We also reveal the eigenvalue distribution of GMMC. Experiments on the ORL, PIE and AR face databases show the effectiveness of the proposed GMMC-based feature extraction methods.
['Gui-Fu Lu', 'Yong Wang', 'Jian Zou']
Graph Maximum Margin Criterion for Face Recognition