Emotion Recognition from Facial Expressions of 4D Videos Using Curves and Surface Normals

In this paper, we propose and compare three methods for recognizing emotions from facial expressions using 4D videos. In the first two methods, the 3D faces are re-sampled by using curves to extract the feature information. Two different methods are presented to resample the faces in an intelligent way using parallel curves and radial curves. The movement of the face is measured through these curves using two frames: neutral and peak frame. The deformation matrix is formed by computing the distance point to point on the corresponding curves of the neutral frame and peak frame. This matrix is used to create the feature vector that will be used for classification using Support Vector Machine (SVM). The third method proposed is to extract the feature information from the face by using surface normals. At every point on the frame, surface normals are extracted. The deformation matrix is formed by computing the Euclidean distances between the corresponding normals at a point on neutral and peak frames. This matrix is used to create the feature vector that will be used for classification of emotions using SVM. The proposed methods are analyzed and they showed improvement over existing literature.