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Mind reading encompasses our ability to attribute mental states to others, and is essential for operating in a complex social environment. The goal in building mind reading machines is to enable computer technologies to understand and react to people's emotions and mental states. This paper describes a system, for the automated inference of cognitive mental states from observed facial expressions and head gestures in video. The system is based on a multilevel dynamic Bayesian network classifier which models cognitive mental states as a number of interacting facial and head displays. Experimental results yield an average recognition rate of 87.4% for 6 mental states groups: agreement, concentrating, disagreement, interested, thinking and unsure. Real time performance, unobtrusiveness and lack of preprocessing make our system particularly suitable for user-independent human computer interaction
Kaliouby et al. (2005) studied this question.