We present an algorithm to quickly analyse and compress facial images using a 2-dimensional morphable model. It runs in real-time on reasonable resources, and offers considerable opportunities for parallelization. A morphable model associates a "shape vector" and a "texture vector" with each image of a sample set. The model is used to analyze a novel image by estimating the model parameters via an optimization procedure. The novel image is compressed by representing it by the set of best match parameters. For real-time performance, we separate the novel image into shape and texture components by computing correspondences between the novel image and a reference image, and match each component separately using eigenspace projection. This approach can be easily parallelized. We improve the speed of algorithm by exploiting the fact that facial correspondence fields are smooth. By computing correspondences only at a number of "feature points" and using interpolation to approximate the dense fields, we drastically reduce the dimensionality of the vectors in the eigenspace, resulting in much smaller compression times. As an added benefit, this system reduces spurious correspondences, since weak features that may confuse the correspondence algorithm are not tracked.