Abstract
Recently, researchers have been modeling three to nine discrete emotions for creating affective recognition systems. However, in every day life, humans use a rich and powerful language for defining a large variety of affective states. Thus, one of the challenging issues in affective computing is to give computers the ability to recognize a variety of affective states using unsupervised methods. In order to explore this possibility, we describe affective postures representing 4 emotion categories using low level descriptors. We applied multivariate analysis to recognize and categorize these postures into nuances of these categories. The results obtained show that low-level posture features may be used for this purpose, leaving the naming issue to interactive processes.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Piller, C.: A Human Touch for Machines- The radical movement affective computing is turning the filed of artificial intelligence upside down by adding emotion to the equation. Los Angeles Times, Los Angeles (2002)
Whissell, C.: The dictionary of affect in language, Emotion: Theory, research and experience, The measurement of emotions, vol. 4. Academic press, New York (1995)
Plutchik, R.: Emotion:A psychoevolutionary synthesis. Harper and Row (1989)
Schröder, M.: Dimensional emotion representation as a basis for speech synthesis with nonextreme emotions. In: André, E., Dybkjær, L., Minker, W., Heisterkamp, P. (eds.) ADS 2004. LNCS (LNAI), vol. 3068, pp. 209–220. Springer, Heidelberg (2004)
Tsapatsoulis, N., Raousaiou, A., Kollias, S., Cowie, R.: Emotion recognition and synthesis based on mpeg-4 faps. In: MPEG-4 Facial Animation - The standard, implementations, applications, pp. 141–167 (2002)
Ruttkay, Z., Noot, H., Hagen, P.: Emotion disc and emotion squares:tools to explore the facial expression space. Computer Graphics Forum 22, 49–53 (2003)
Ekman, P.: Emotion in the Human Face. Cambridge University Press, Cambridge (1982)
Argyle, M.: Bodily Communication. Methuen & Co. Ltd, London (1988)
Coulson, M.: Attributing emotion to static body postures: recognition accuracy, confusions, and viewpoint dependence. Journ. of Nonver. Behav. 28, 117–139 (2004)
Kleinsmith, A., de Silva, P.R., Bianchi-Berthouze, N.: Recognizing emotion from postures: Cross-cultural differences in user modeling. In: Ardissono, L., Brna, P., Mitrović, A. (eds.) UM 2005. LNCS (LNAI), vol. 3538, pp. 50–59. Springer, Heidelberg (2005)
Bianchi-Berthouze, N., Kleinsmith, A.: A categorical approach to affective gesture recognition. Connection Science special issue on Epigenetic Robotics - Modeling Cognitive Development in Robotic Systems 15, 259–269 (2003)
De Silva, R., Bianchi-Berthouze, N.: Modeling human affective postures: An information theoretic characterization of posture features. Journal of Computer Animation and Virtual Worlds 15, 269–276 (2004)
Hastie, T., Tibshirabi, R.: Discriminant analysis by gaussian mixture. Journal of the Royal Statistical Society, B, 155–176 (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
De Silva, P.R., Kleinsmith, A., Bianchi-Berthouze, N. (2005). Towards Unsupervised Detection of Affective Body Posture Nuances. In: Tao, J., Tan, T., Picard, R.W. (eds) Affective Computing and Intelligent Interaction. ACII 2005. Lecture Notes in Computer Science, vol 3784. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573548_5
Download citation
DOI: https://doi.org/10.1007/11573548_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29621-8
Online ISBN: 978-3-540-32273-3
eBook Packages: Computer ScienceComputer Science (R0)