Predicting Group Cohesiveness in Images

Shreya Ghosh, Abhinav Dhall, Nicu Sebe, Tom Gedeon

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

17 Citations (Scopus)


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. 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.

Original languageEnglish
Title of host publicationInternational Joint Conference on Neural Networks (IJCNN) 2019
EditorsPlamen Angelov, Manuel Roveri
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781728119854
ISBN (Print)9781728119861
Publication statusPublished - 2019
Externally publishedYes
EventIEEE International Joint Conference on Neural Networks 2019 - Budapest, Hungary
Duration: 14 Jul 201919 Jul 2019 (Proceedings)


ConferenceIEEE International Joint Conference on Neural Networks 2019
Abbreviated titleIJCNN 2019
Internet address

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