Facial expression recognition and its degree estimation

The purpose of this study is not only to recognize a facial expression which is associated with human emotion but also to estimate its degree. Our method is based on the idea that facial expression recognition can be achieved by extracting a variation from an expressionless face considering the face area as a whole pattern. For the purpose of extracting subtle changes in the face such as the degree of expressions, it is necessary to eliminate the individuality appearing in the facial image. Using an elastic net model, a variation of facial expression is represented as motion vectors of the deformed net from a facial edge image. Then, applying K-L expansion, the change of facial expression represented as the motion vectors of nodes is mapped into a low dimensional eigenspace, and estimation is achieved by projecting input images on to the emotion space. We have constructed three kinds of expression models; happiness, anger, surprise, and experimental results are evaluated