The More the Merrier: Analysing the Affect of a Group of People in Images

Abstract

The recent advancement of social media has given users a platform to socially engage and interact with a global population. With millions of images being uploaded onto social media platforms, there is an increasing interest in inferring the emotion and mood display of a group of people in images. Automatic affect analysis research has come a long way but has traditionally focussed on a single subject in a scene. In this paper, we study the problem of inferring the emotion of a group of people in an image. This group affect has wide applications in retrieval, advertisement, content recommendation and security. The contributions of the paper are: 1) a novel emotion labelled database of groups of people in images; 2) a Multiple Kernel Learning based hybrid affect inference model; 3) a scene context based affect inference model; 4) a user survey to better understand the attributes that affect the perception of affect of a group of people in an image. The detailed experimentation validation provides a rich baseline for the proposed database