  Group cohesiveness is a compelling and often studied composition in group
dynamics and group performance. The enormous number of web images of groups of
people can be used to develop an effective method to detect group cohesiveness.
This paper introduces an automatic group cohesiveness prediction method for the
7th Emotion Recognition in the Wild (EmotiW 2019) Grand Challenge in the
category of Group-based Cohesion Prediction. The task is to predict the
cohesive level for a group of people in images. To tackle this problem, a
hybrid network including regression models which are separately trained on face
features, skeleton features, and scene features is proposed. Predicted
regression values, corresponding to each feature, are fused for the final
cohesive intensity. Experimental results demonstrate that the proposed hybrid
network is effective and makes promising improvements. A mean squared error
(MSE) of 0.444 is achieved on the testing sets which outperforms the baseline
MSE of 0.5.
