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  • Research Article
  • Open Access

Automatic Detection of Dominance and Expected Interest

  • 1, 2Email author,
  • 1, 2,
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EURASIP Journal on Advances in Signal Processing20102010:491819

  • Received: 3 August 2009
  • Accepted: 17 March 2010
  • Published:


Social Signal Processing is an emergent area of research that focuses on the analysis of social constructs. Dominance and interest are two of these social constructs. Dominance refers to the level of influence a person has in a conversation. Interest, when referred in terms of group interactions, can be defined as the degree of engagement that the members of a group collectively display during their interaction. In this paper, we argue that only using behavioral motion information, we are able to predict the interest of observers when looking at face-to-face interactions as well as the dominant people. First, we propose a simple set of movement-based features from body, face, and mouth activity in order to define a higher set of interaction indicators. The considered indicators are manually annotated by observers. Based on the opinions obtained, we define an automatic binary dominance detection problem and a multiclass interest quantification problem. Error-Correcting Output Codes framework is used to learn to rank the perceived observer's interest in face-to-face interactions meanwhile Adaboost is used to solve the dominant detection problem. The automatic system shows good correlation between the automatic categorization results and the manual ranking made by the observers in both dominance and interest detection problems.


  • Social Construct
  • Detection Problem
  • Output Code
  • Code Framework
  • Emergent Area

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Authors’ Affiliations

Computer Vision Center, Campus UAB, Edifici O, 08193 Bellaterra, Spain
Departament de Matemàtica Aplicada i Anàlisi, Universitat de Barcelona, Gran Via de les Corts Catalanes 585, 08007 Barcelona, Spain
Departament de Metodologia de les Ciències del Comportament, Universitat de Barcelona, Gran Via de les Corts Catalanes 585, 08007 Barcelona, Spain


© Sergio Escalera et al. 2010

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.