A model-based facial expression recognition algorithm using Principal Components Analysis

In this paper, we propose a new method for facial expression recognition. We utilize the Candide facial grid and apply principal components analysis (PCA) to find the two eigenvectors of the model vertices. These eigenvectors along with the barycenter of the vertices are used to define a new coordinate system where vertices are mapped. Support vector machines (SVMs) are then used for the facial expression classification task. The method is invariant to in-plane translation and rotation as well as scaling of the face and achieves very satisfactory results.