PT Unknown
AU Sergio Escalera
   Oriol Pujol
   Petia Radeva
   Jordi Vitria
TI Measuring Interest of Human Dyadic Interactions
BT 12th International Conference of the Catalan Association for Artificial Intelligence
PY 2009
BP 45
EP 54
VL 202
DI 10.3233/978-1-60750-061-2-45
AB 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.
ER