Affective computing
Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward Machine Emotional Intelligence: Analysis of Affective Physiological State
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Affective multimodal human-computer interaction
Proceedings of the 13th annual ACM international conference on Multimedia
Making sense of student use of nonverbal cues for intelligent tutoring systems
OZCHI '05 Proceedings of the 17th Australia conference on Computer-Human Interaction: Citizens Online: Considerations for Today and the Future
Automatic prediction of frustration
International Journal of Human-Computer Studies
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Evaluating an affective student model for intelligent learning environments
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
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Human-to-computer interaction in a variety of applications could benefit if systems could accurately analyze and respond to their users' affect. Although a great deal of research has been conducted on affect recognition, very little of this work has considered what is the appropriate information to extract in specific situations. Towards understanding how specific applications such as affective tutoring and affective entertainment could benefit, we present two experiments. In the first experiment, we found that students' facial expressions, together with their body actions, gave little information about their internal emotion per se but they would be useful features for predicting their self-reported "true" mental state. In the second experiment, we found significant differences between the facial expressions and self-reported affective state of viewers watching a movie sequence. Our results suggest that the noisy relationship between observable gestures and underlying affect must be accounted for when designing affective computing applications.