Generic active appearance models revisited

The proposed Active Orientation Models (AOMs) are gen- erative models of facial shape and appearance. Their main dierences with the well-known paradigm of Active Appearance Models (AAMs) are (i) they use a dierent statistical model of appearance, (ii) they are accompanied by a robust algorithm for model tting and parameter es- timation and (iii) and, most importantly, they generalize well to unseen faces and variations. Their main similarity is computational complex- ity. The project-out version of AOMs is as computationally ecient as the standard project-out inverse compositional algorithm which is ad- mittedly the fastest algorithm for tting AAMs. We show that not only does the AOM generalize well to unseen identities, but also it outper- forms state-of-the-art algorithms for the same task by a large margin. Finally, we prove our claims by providing Matlab code for reproducing our experiments.