Radial-curve-based facial expression recognition

A fully automatic facial-expression recognition (FER) system on 3D expression mesh models was proposed. The system didn't need human interaction from the feature extraction stage till the facial expression classification stage. The features extracted from a 3D expression mesh model were a bunch of radial facial curves to represent the spatial deformation of the geometry features on human face. Each facial curve was a surface line on the 3D face mesh model, begun from the nose tip and ended at the boundary of the previously trimmed 3D face points cloud. Then Euclid distance was employed to calculate the difference between facial curves extracted from the neutral face and each face with different expressions of one person as feature. By employing support vector machine (SVM) as classifier, the experimental results on the well-known 3D-BUFE dataset indicate that the proposed system could better classify the six prototypical facial expressions than state-of-art algorithms.