Utilizing 3D flow of points for facial expression recognition

This paper presents an approach to recognize Facial Expressions of different intensities using 3D flow of facial points. 3D flow is the geometrical displacement (in 3D) of a facial point from its position in a neutral face to that in the expressive face. Experiments are performed on 3D face models from the BU-3DFE database. Four different intensities of expressions are used for analyzing the relevance of intensity of the expression for the task of FER. It was observed that high intensity expressions are easier to recognize and there is a need to develop algorithms for recognizing low intensity facial expressions. The proposed features outperform difference of facial distances and 2D optical flow. Performances of two classifiers, SVM and LDA are compared wherein SVM performs better. Feature selection did not prove useful.