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Open Access

Automatic Detection of Dominance and Expected Interest

  • Sergio Escalera1, 2Email author,
  • Oriol Pujol1, 2,
  • Petia Radeva1, 2,
  • Jordi Vitrià1, 2 and
  • M. Teresa Anguera3
EURASIP Journal on Advances in Signal Processing20102010:491819

Received: 3 August 2009

Accepted: 17 March 2010

Published: 2 June 2010


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 ConstructDetection ProblemOutput CodeCode FrameworkEmergent Area

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Authors’ Affiliations

Computer Vision Center, Bellaterra, Spain
Departament de Matemàtica Aplicada i Anàlisi, Universitat de Barcelona, Barcelona, Spain
Departament de Metodologia de les Ciències del Comportament, Universitat de Barcelona, 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.