Towards Unsupervised Detection of Affective Body Posture Nuances

  • P. Ravindra De Silva
  • Andrea Kleinsmith
  • Nadia Bianchi-Berthouze
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3784)


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.


Emotion Recognition Gesture Recognition Basic Emotion Emotion Category Unsupervised Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • P. Ravindra De Silva
    • 1
  • Andrea Kleinsmith
    • 1
  • Nadia Bianchi-Berthouze
    • 1
  1. 1.Database Systems LaboratoryUniversity of AizuAizu WakamatsuJapan

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