Automatic Group Level Affect and Cohesion Prediction in Videos

This paper proposes a database for group level emotion recognition in videos. The motivation is coming from the large number of information which the users are sharing online. This gives us the opportunity to use this perceived affect for various tasks. Most of the work in this area has been restricted to controlled environments. In this paper, we explore the group level emotion and cohesion in a real-world environment. There are several challenges involved in moving from a controlled environment to real-world scenarios such as face tracking limitations, illumination variations, occlusion and type of gatherings. As an attempt to address these challenges, we propose a 'Video level Group AFfect (VGAF), database containing 1, 004 videos downloaded from the web. The collected videos have a large variations in terms of gender, ethnicity, the type of social event, number of people, pose, etc. We have labelled our database for group level emotion and cohesion tasks and proposed a baseline based on the Inception V3 network on the database.