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Your fellows matter: Affect analysis across subjects in group videos

Accepted version
Peer-reviewed

Type

Conference Object

Change log

Authors

Mou, W 
Patras, I 

Abstract

Automatic affect analysis has become a well established research area in the last two decades. Recent works have started moving from individual to group scenarios. However, little attention has been paid to investigating how individuals in a group influence the affective states of each other. In this paper, we propose a novel framework for cross-subjects affect analysis in group videos. Specifically, we analyze the correlation of the affect among group members and investigate the automatic recognition of the affect of one subject using the behaviours expressed by another subject in the same group. A set of experiments are conducted using a recently collected database aimed at affect analysis in group settings. Our results show that (1) people in the same group do share more information in terms of behaviours and emotions than people in different groups; and (2) the affect of one subject in a group can be better predicted using the expressive behaviours of another subject within the same group than using that of a subject from a different group. This work is of great importance for affect recognition in group settings: when the information of one subject is unavailable due to occlusion, head/body poses etc., we can predict his/her affect by employing the expressive behaviours of the other subject(s).

Description

Keywords

46 Information and Computing Sciences, 4608 Human-Centred Computing

Journal Title

Proceedings - 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019

Conference Name

2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019)

Journal ISSN

2326-5396

Volume Title

Publisher

IEEE
Sponsorship
European Commission Horizon 2020 (H2020) Societal Challenges (826232)
European Unions Horizon 2020