This paper introduces a novel approach to social behaviour recognition governed by the exchange of non-verbal cues between people. We conduct experiments to try and deduce distinct rules that dictate the social dynamics of people in a conversation, and utilise semi-supervised computer vision techniques to extract their social signals such as laughing and nodding. Data mining is used to deduce frequently occurring patterns of social trends between a speaker and listener in both interested and not interested social scenarios. The confidence values from rules are utilised to build a Social Dynamic Model (SDM), that can then be used for classification and visualisation. By visualising the rules generated in the SDM, we can analyse distinct social trends between an interested and not interested listener in a conversation. Results show that these distinctions can be applied generally and used to accurately predict conversational interest.

We introduce a new algorithm for real-time interactive motion control and demonstrate its application to motion captured data, prerecorded videos, and HCI. Firstly, a data set of frames are projected into a lower dimensional space. An appearance model is learnt using a multivariate probability distribution. A novel approach to determining transition points is presented based on k-medoids, whereby appropriate points of intersection in the motion trajectory are derived as cluster centers. These points are used to segment the data into smaller subsequences. A transition matrix combined with a kernel density estimation is used to determine suitable transitions between the subsequences to develop novel motion. To facilitate real-time interactive control, conditional probabilities are used to derive motion given user commands. The user commands can come from any modality including auditory, touch, and gesture. The system is also extended to HCI using audio signals of speech in a conversation to trigger nonverbal responses from a synthetic listener in real-time. We demonstrate the flexibility of the model by presenting results ranging from data sets composed of vectorized images, 2-D, and 3-D point representations. Results show real-time interaction and plausible motion generation between different types of movement.