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Toward an affect-sensitive multimodal human-computer interaction

The ability to recognize affective states of a person we are communicating with is the core of emotional intelligence. Emotional intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for successful interpersonal social interaction. This...

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Published in:Proceedings of the IEEE 2003-09, Vol.91 (9), p.1370-1390
Main Authors: Pantic, M., Rothkrantz, L.J.M.
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Language:English
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description The ability to recognize affective states of a person we are communicating with is the core of emotional intelligence. Emotional intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for successful interpersonal social interaction. This paper argues that next-generation human-computer interaction (HCI) designs need to include the essence of emotional intelligence - the ability to recognize a user's affective states-in order to become more human-like, more effective, and more efficient. Affective arousal modulates all nonverbal communicative cues (facial expressions, body movements, and vocal and physiological reactions). In a face-to-face interaction, humans detect and interpret those interactive signals of their communicator with little or no effort. Yet design and development of an automated system that accomplishes these tasks is rather difficult. This paper surveys the past work in solving these problems by a computer and provides a set of recommendations for developing the first part of an intelligent multimodal HCI-an automatic personalized analyzer of a user's nonverbal affective feedback.
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source IEEE Electronic Library (IEL) Journals
subjects Animation
Anthropometry
Cognitive science
Design engineering
Emotion recognition
Emotional intelligence
Emotions
Face detection
Facial
Feedback
Human
Human computer interaction
Intelligence
Interactive
Knowledge based systems
Movements
Neuroscience
Psychology
Recognition
Social interaction
Tasks
title Toward an affect-sensitive multimodal human-computer interaction
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