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Regression algorithm for emotion detection

Franck Berthelon 1 Peter Sander 1
1 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : We present here two components of a computational system for emotion detection. PEMs (Personalized Emotion Maps) store links between bodily expressions and emotion values, and are individually calibrated to capture each person's emotion profile. They are an implementation based on aspects of Scherer's theoretical complex system model of emotion~\cite{scherer00, scherer09}. We also present a regression algorithm that determines a person's emotional feeling from sensor measurements of their bodily expressions, using their individual PEMs. The aim of this architecture is to dissociate sensor measurements of bodily expression from the emotion expression interpretation, thus allowing flexibility in the choice of sensors. We test the prototype system using video sequences of facial expressions and demonstrate the real-time capabilities of the system for detecting emotion. We note that, interestingly, the system detects the sort of hysteresis phenomenon in changing emotional state as suggested by Scherer's psychological model.
keyword : emotion regression
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Submitted on : Sunday, November 24, 2013 - 6:04:29 PM
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Franck Berthelon, Peter Sander. Regression algorithm for emotion detection. CogInfoCom'13 - The 4th IEEE International Conference on Cognitive Infocommunicaitons, Dec 2013, Budapest, Hungary. ⟨hal-00908542⟩

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