In this paper, we describe image-based modeling techniques that make possible the creation of photo-realistic computer models of real human faces. The image-based model is built using example views of the face, bypassing the need for any three-dimensional computer graphics models. A learning network is trained to associate each of the example images with a set of pose and expression parameters. For a novel set of parameters, the network synthesizes a novel, intermediate view using a morphing approach. This image-based synthesis paradigm can adequately model both rigid and non-rigid facial movements. We also describe an analysis-by-synthesis algorithm, which is capable of extracting a set of high-level parameters from an image sequence involving facial movement using embedded image-based models. The parameters of the models are perturbed in a local and independent manner for each image until a correspondence-based error metric is minimized. A small sample of experimental results is presented.