In this paper, we address the analysis and recognition of facial expressions in continuous videos. We introduce a view- and texture-independent approach that exploits the temporal facial action parameters estimated by an appearance-based 3D face tracker. The facial expression recognition is carried out using learned dynamical models based on auto-regressive processes. These learned models can also be utilized for the synthesis and prediction tasks. Experiments demonstrated the effectiveness of the developed method.