An empirical study of machine learning techniques for affect recognition in human-robot interaction

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Abstract Given the importance of implicit communication in human interactions, it would be valuable to have this capability in robotic systems wherein a robot can detect the motivations and emotions of the person it is working with. Recognizing affective states from physiological cues is an effective way of implementing implicit human

Pramila Rani, Changchun Liu, Nilanjan Sarkar, Eric

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Affective State | Distributed And Parallel Computing | Implicit Humanrobot Interaction | Machine | PAA 2006 |

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Added	14 Dec 2010
Updated	14 Dec 2010
Type	Journal
Year	2006
Where	PAA
Authors	Pramila Rani, Changchun Liu, Nilanjan Sarkar, Eric Vanman

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An empirical study of machine learning techniques for affect recognition in human-robot interaction

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An empirical study of machine learning techniques for affect recognition in human-robot interaction

Affective State | Distributed And Parallel Computing | Implicit Human­robot Interaction | Machine | PAA 2006 |

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Affective State | Distributed And Parallel Computing | Implicit Humanrobot Interaction | Machine | PAA 2006 |