Automatic role recognition in multiparty recordings: using social affiliation networks for feature extraction

Salamin, H., Favre, S. and Vinciarelli, A. (2009) Automatic role recognition in multiparty recordings: using social affiliation networks for feature extraction. IEEE Transactions on Multimedia, 11(7), pp. 1373-1380. (doi:10.1109/TMM.2009.2030740)

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Abstract

Automatic analysis of social interactions attracts increasing attention in the multimedia community. This letter considers one of the most important aspects of the problem, namely the roles played by individuals interacting in different settings. In particular, this work proposes an automatic approach for the recognition of roles in both production environment contexts (e.g., news and talk-shows) and spontaneous situations (e.g., meetings). The experiments are performed over roughly 90 h of material (one of the largest databases used for role recognition in the literature) and show that the recognition effectiveness depends on how much the roles influence the behavior of people. Furthermore, this work proposes the first approach for modeling mutual dependences between roles and assesses its effect on role recognition performance.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Vinciarelli, Professor Alessandro
Authors: Salamin, H., Favre, S., and Vinciarelli, A.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science
Journal Name:IEEE Transactions on Multimedia
Publisher:IEEE
ISSN:1520-9210
ISSN (Online):1941-0077

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