Measuring Interest of Human Dyadic Interactions

Authors:
Sergio Escalera;Oriol Pujol;Petia Radeva;Jordi Vitrià
Affiliations:
Computer Vision Center, Spain;Computer Vision Center, Spain;Computer Vision Center, Spain;Computer Vision Center, Spain
Venue:
Proceedings of the 2009 conference on Artificial Intelligence Research and Development: Proceedings of the 12th International Conference of the Catalan Association for Artificial Intelligence
Year:
2009

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Abstract

In this paper, we argue that only using behavioural motion information, we are able to predict the interest of observers when looking at face-to-face interactions. We propose a set of movement-related features from body, face, and mouth activity in order to define a set of higher level interaction features, such as stress, activity, speaking engagement, and corporal engagement. Error-Correcting Output Codes framework with an Adaboost base classifier is used to learn to rank the perceived observer's interest in face-to-face interactions. The automatic system shows good correlation between the automatic categorization results and the manual ranking made by the observers. In particular, the learning system shows that stress features have a high predictive power for ranking interest of observers when looking at of face-to-face interactions.