A local-to-holistic face recognition approach using elastic graph matching

A local-to-holistic face recognition approach is proposed, which is an extension to elastic graph matching. In the first step, the facial features such as eyes, mouth and nose, are detected by an eigenface method, so we can set up the local feature model (eye, mouth and nose) and the holistic feature model (inner face). In the second step, an elastic graph-matching (EGM) algorithm is performed not only on holistic features, but also on local features. An experiment on the Yale face database shows the performance of the approach.