Social Influence Analysis based on Facial Emotions

In this paper, we propose a method to analyse the social correlation among the group of people in any small gathering; such as business meetings, group discussion, etc.; Within such networks, correlation is build based on the tracked facial emotions of all the individuals in the network. The facial emotional feature extraction is based on active appearance model; whereas the approach for emotion detection lies in the dynamic and probabilistic framework of deep belief networks. Combining active appearance model with deep belief networks for emotion recognition gives higher recognition performance level compared with other methods. The analysis of change in facial emotions of all the individuals in the group help us to understand the hidden correlation among them, which is not observable with the naked eyes. Finally, we evaluate the system by comparing the results with ground truth of a scripted discussion. Also, the results obtained by our system effectively reflects the emotion propagation in the scripted discussion.