Abstract

Automatic facial expression recognition has now advanced to the point that we are able to apply it to spontaneous expressions. The automated tools enable new research activity in cognitive neuroscience, psychiatry, education, human–machine communication, and human social dynamics. Moreover, automated facial expression analysis enables investigations into facial expression dynamics that were previously intractable by human coding because of the time required to code intensity changes. This article provides an overview on the state of the art in computer vision approaches to facial expression recognition, including methods for characterizing expression dynamics. It then discusses behavioral studies that have employed automatic facial expression recognition to learn new information about the relationships of facial expression to internal state, and reviews the first generation of applications in learning and education that take advantage of the real-time expression signal.