An Active Appearance Model (AAM) allows complex models of shape and appearance to be matched to new images rapidly. An AAM contains a statistical model of the shape and grey-level appearance of an object of interest. The associated search algorithm exploits the locally linear relationship between model parameter displacements and the residual errors between model instance and image. This relationship can be learnt during a training phase. To match to an image we measure the current residuals and use the model to predict changes to the current parameters. The algorithm converges in a few iterations. In this paper we describe variations of the basic algorithm aimed at improving the speed and robustness of search. These include sub-sampling and using image residuals to drive the shape rather than full appearance model. We show examples of search and give the results of experiments comparing the performance of the different algorithms.
