Automatic prediction of group cohesiveness in images

Shreya Ghosh, Abhinav Dhall, Nicu Sebe, Tom Gedeon

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)


This paper 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 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. 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 can 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. Further, we investigate the effect of face-level similarity, body pose and subset of group on the task of automatic cohesion perception.

Original languageEnglish
Number of pages13
JournalIEEE Transactions on Affective Computing
Publication statusAccepted/In press - 23 Sep 2020


  • Cohesion estimation
  • Group-level emotion

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