Face recognition using Fisherface algorithm and elastic graph matching

This paper proposes a face recognition technique that effectively combines elastic graph matching (EGM) and the Fisherface algorithm. EGM as one of the dynamic link architectures uses not only face-shape but also the gray information of image, and the Fisherface algorithm as a class-specific method is robust about variations such as lighting direction and facial expression. In the proposed face recognition adopting the above two methods, the linear projection per node of an image graph reduces the dimensionality of labeled graph vector and provides a feature space to be used effectively for the classification. In comparison with the conventional method, the proposed approach could obtain satisfactory results from the perspectives of recognition rates and speeds. In particular, we could get maximum recognition rate of 99.3% by the leaving-one-out method for experiments with the Yale face databases.