Dynamic facial expression recognition by joint static and multi-time gap transition classification

Automatic facial expression classification is a challenging problem for developing intelligent human-computer interaction systems. In order to take into account the expression dynamics, existing works usually make the assumption that a specific facial expression is displayed with a pre-segmented evolution, i.e. starting from neutral and finishing on an apex frame. In this paper, we propose a method to train a transition classifier from pairs of images. This transition classifier is applied at multiple time gaps and the output probabilities are fused along with a static estimation. We eventually show that our approach yields state-of-the-art accuracy on popular datasets without exploiting any such prior on the segmentation of the expression.