View-Based Active Appearance Models

We demonstrate that a small number of 2D statistical models are sufficient to capture the shape and appearance of a face from any viewpoint (full profile to fronto-parallel). Each model is linear and can be matched rapidly to new images using the Active Appearance Model algorithm. We show how such a set of models can be used to estimate head pose, to track faces through large angles of head rotation and to synthesize faces from unseen viewpoints.