# Predicting Group Cohesiveness in Images

## TL;DR

This paper introduces a neural network-based approach to estimate group cohesiveness from images, combining user surveys and novel databases, achieving near human-level accuracy and revealing a correlation with group emotion.

## Contribution

It proposes a multi-task CNN and capsule network for predicting group cohesion, introduces the GAF-Cohesion database, and demonstrates the link between group emotion and cohesion.

## Key findings

- Model achieves near human-level performance
- Group cohesion correlates with group emotion
- Introduces the GAF-Cohesion database

## 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 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.