Monocular 3D facial information retrieval for automated facial expression analysis

Understanding social signals is a very important aspect of human communication and interaction and has therefore attracted increased attention from various research areas. Among the different types of social signals, particular attention has been paid to facial expression of emotions and its automated analysis from image sequences. Automated facial expression analysis is a very challenging task due to the complex three-dimensional deformation and motion of the face associated to the facial expressions and the loss of 3D information during the image formation process. As a consequence, retrieving 3D spatio-temporal facial information from image sequences is essential for automated facial expression analysis. In this paper, we propose a framework for retrieving three-dimensional facial structure, motion and spatio-temporal features from monocular image sequences. First, we estimate monocular 3D scene flow by retrieving the facial structure using shape-from-shading (SFS) and combine it with 2D optical flow. Secondly, based on the retrieved structure and motion of the face, we extract spatio-temporal features for automated facial expression analysis. Experimental results illustrate the potential of the proposed 3D facial information retrieval framework for facial expression analysis, i.e. facial expression recognition and facial action-unit recognition on a benchmark dataset. This paves the way for future research on monocular 3D facial expression analysis.