Simultaneous tracking and facial expression recognition using multiperson and multiclass autoregressive models

The dynamical recognition of facial gestures and expressions in image sequences is an important and challenging problem. Most of the existing methods adopt the following paradigm. First, facial feature movements are retrieved from the images, then the facial expression is recognized based on these retrieved movements. In contrast to this mainstream approach, this paper introduces a new approach allowing the simultaneous retrieval of facial feature movements and expression using a particle filter adopting multi-class and multi-person dynamics. The proposed fast scheme is either as robust as, or more robust than existing ones in a number of respects. We provide evaluations of performance to show the feasibility and robustness of the proposed approach.