Comparing variations on the active appearance model algorithm

The Active Appearance Model (AAM) algorithm has proved to be a suc-cessful method for matching statistical models of appearance to new images. Since the original algorithm was described there have been a variety of sug-gested modifications to the basic algorithm, each typically claiming to be in some way superior. We review these algorithms and report the results of ex-periments comparing their performance. We also investigate the effects of different methods of estimating the update matrix used in the algorithm. We find that careful choice of the latter has at least as much effect as the choice of updating technique.