MANIFOLD LEARNING FOR SIMULTANEOUS POSE AND FACIAL EXPRESSION RECOGNITION

Research on facial expression recognition has steadily been moving from analysis of deliberative frontal expressions to analysis of unconstrained spontaneous expressions. This shift has spawned complex 3D models and computationally expensive geometric methods that prevent usage on resource constrained platforms such as smart phones. This paper presents manifold learning techniques for accurate multi-view facial expression on low resolution 2D images. Our results indicate that mixed class local pose and expression manifold methods perform better than global expression techniques and work just as well as fusing together results from multiple manifolds.