Abstract:
The cohesiveness of a group is an essential indicator
of the emotional state, structure and success of a group of people.
We study the factors that influence the perception of grouplevel cohesion and propose methods for estimating the humanperceived cohesion on the group cohesiveness scale. In order to identify the visual cues (attributes) for cohesion, we conducted
a user survey. Image analysis is performed at a group-level via
a multi-task convolutional neural network. For analyzing the
contribution of facial expressions of the group members for
predicting the Group Cohesion Score (GCS), a capsule network
is explored. We add GCS to the Group Affect database and
propose the ‘GAF-Cohesion database’. The proposed model
performs well on the database and is able to achieve near
human-level performance in predicting a group’s cohesion score.
It is interesting to note that group cohesion as an attribute,
when jointly trained for group-level emotion prediction, helps
in increasing the performance for the later task. This suggests
that group-level emotion and cohesion are correlated.