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Learning spatio-temporal models of facial expressions

The human face is used to regulate the conversation by gazing or nodding, to interpret what has been said by lip reading, and to communicate and understand somebody’s affective state and intentions on the basis of the shown facial expression. Machine understanding of human facial behavior could revolutionize human-machine interaction technologies and fields as diverse as security, behavioral science, medicine, communication, and education. Yet development of an automated system that detects and interprets human facial signals is rather difficult. This article summarizes our research efforts in meeting this challenge. It presents two systems for machine recognition of facial muscle actions (i.e., Action Units, AUs) in face video and a case-based reasoning system capable of classifying facial expressions (coded in terms of AUs) into the emotion categories learned from the user.