Predicting Group Cohesiveness in Images.

Cohesiveness of a group is an essential indicator of emotional state, structure and success of a group of people. We study the factors that influence the perception of group level cohesion and propose methods for estimating the human-perceived cohesion on the Group Cohesiveness Scale (GCS). 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 GCS, capsule network is explored. In order to identify the visual cues (attributes) for cohesion, we conducted a user survey. Based on the Group Affect database, we add GCS 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 group's cohesion score. It is interesting to note that GCS 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 GCS are correlated.