Towards Unsupervised Detection of Affective Body Posture Nuances

Abstract

Conference dates: 22-24 october 2005

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.