Automatic Prediction of Group Cohesiveness in Images

Abstract

This article discusses the prediction of cohesiveness of a group of people in images. The cohesiveness of a group is an essential indicator of the emotional state, structure, and success of the group. 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. 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. A capsule network is explored for analyzing the contribution of facial expressions of the group members on predicting the Group Cohesion Score (GCS). We add GCS to the Group Affect database and propose the ‘GAF-Cohesion database’. The proposed model performs well on the database and achieves 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. Further, we investigate the effect of face-level similarity, body pose and subset of a group on the task of automatic cohesion perception.