In this paper we present a fully automatic approach for identity-independent facial expression recognition from 3D video sequences. Towards that goal, we propose a novel approach to extract a scalar field that represents the defor- mations between faces conveying different expressions. We extract relevant features from this deformation field using LDA and then train a dynamic model on these features using HMM. Experiments conducted on BU-4DFE dataset fol- lowing state-of-the-art settings show the effectiveness of the proposed approach.