Abstract
In this paper we present a novel framework for analysing non-stylised motion in order to detect implicitly communicated affect. Our approach makes use of a segmentation technique which can divide complex motions into a set of automatically derived motion primitives. The parsed motion is then analysed in terms of dynamic features which are shown to encode affective information. In order to adapt our algorithm to personal movement idiosyncrasies we developed a new approach for deriving unbiased motion features. We have evaluated our approach using a comprehensive database of affectively performed motions. The results show that removing personal movement bias can have a significant benefit for automated affect recognition from body motion. The resulting recognition rate is similar to that of humans who took part in a comparable psychological experiment.
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Bernhardt, D., Robinson, P. (2007). Detecting Affect from Non-stylised Body Motions. In: Paiva, A.C.R., Prada, R., Picard, R.W. (eds) Affective Computing and Intelligent Interaction. ACII 2007. Lecture Notes in Computer Science, vol 4738. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74889-2_6
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DOI: https://doi.org/10.1007/978-3-540-74889-2_6
Publisher Name: Springer, Berlin, Heidelberg
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