We address the problem of recognizing the visual focus of attention (VFOA) of meeting participants from their head pose and contextual cues. The main contribution of the paper is the use of a head pose posterior distribution as a representation of the head pose information contained in the image data. This posterior encodes the probabilities of the different head poses given the image data, and constitute therefore a richer representation of the data than the mean or the mode of this distribution, as done in all previous work. These observations are exploited in a joint interaction model of all meeting participants pose observations, VFOAs, speaking status and of environmental contextual cues. Numerical experiments on a public database of 4 meetings of 22min on average show that this change of representation allows for a 5.4% gain with respect to the standard approach using head pose as observation.