Pools of AAMs: Towards Automatically Fitting any Face Image
Julien Peyras, Adrien Bartoli, S.K. Khoualed · 2008
Fitting a single generic AAM on an unseen face (that is not in the training set) under any pose and expression is very difficult. The v ariability of the data is so high that the fitting process usually gets stuck into one of the numerous local minima. We show that a solution to this problem consists to separate the variability sources. We build a pool of specialized AAMs. Each AAM is trained over multiple identities, all shown under the same pose and expression. We then retain the AAM that shows the smallest residual error when fitted to the input image. The fitting obtained in th is manner is very accurate on unseen faces. The ultimate goal is to automatically train a person-specific AAM. In addition, the pool of specialized AA Ms allows us to recognize the face pose and expression at each frame of the video with good performances. The proposed method has potential applications in Human Computer Interaction and driving surveillance, to name just but a few.