Abstract

In this paper, we propose a new scheme to formulate the dy- namic facial expression recognition problem as a longitudinal atlases con- struction and deformable groupwise image registration problem. The main contributions of this method include: 1) We model human facial feature changes during the facial expression process by a diffeomorphic image reg- istration framework; 2) The sub ject-specific longitudinal change informa- tion of each facial expression is captured by building an expression growth model; 3) Longitudinal atlases of each facial expression are constructed by performing groupwise registration among all the corresponding expression image sequences of different sub jects. The constructed atlases can reflect overall facial feature changes of each expression among the population, and can suppress the bias due to inter-personal variations. The proposed method was extensively evaluated on the Cohn-Kanade, MMI, and Oulu- CASIA VIS dynamic facial expression databases and was compared with several state-of-the-art facial expression recognition approaches. Exper- imental results demonstrate that our method consistently achieves the highest recognition accuracies among other methods under comparison on all the databases.