Abstract:
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.
