Human expression recognition from motion using a radial basis function network architecture

In this paper a radial basis function network architecture is developed that learns the correlation of facial feature motion patterns and human expressions. We describe a hierarchical approach which at the highest level identifies expressions, at the mid level determines motion of facial features, and at the low level recovers motion directions. Individual expression networks were trained to recognize the "smile" and "surprise" expressions. Each expression network was trained by viewing a set of sequences of one expression for many subjects. The trained neural network was then tested for retention, extrapolation, and rejection ability. Success rates were 88% for retention, 88% for extrapolation, and 83% for rejection.