ARBEE: Towards Automated Recognition of Bodily Expression of Emotion In the Wild

TL;DR

This paper introduces BoLD, a large dataset of body movements for emotion recognition, and ARBEE, a system leveraging Laban Movement Analysis features to recognize bodily expressions of emotion in unconstrained environments.

Contribution

The paper presents a new large-scale in-the-wild dataset and a novel system for recognizing emotional body language, advancing automatic emotion recognition from body movements.

Findings

LMA features effectively characterize arousal in body movements.

The dataset contains 9,876 videos with over 13,000 annotated human characters.

Baseline methods show promising results in emotion recognition from body data.

Abstract

Humans are arguably innately prepared to comprehend others' emotional expressions from subtle body movements. If robots or computers can be empowered with this capability, a number of robotic applications become possible. Automatically recognizing human bodily expression in unconstrained situations, however, is daunting given the incomplete understanding of the relationship between emotional expressions and body movements. The current research, as a multidisciplinary effort among computer and information sciences, psychology, and statistics, proposes a scalable and reliable crowdsourcing approach for collecting in-the-wild perceived emotion data for computers to learn to recognize body languages of humans. To accomplish this task, a large and growing annotated dataset with 9,876 video clips of body movements and 13,239 human characters, named BoLD (Body Language Dataset), has been created. Comprehensive statistical analysis of the dataset revealed many interesting insights. A system to model the emotional expressions based on bodily movements, named ARBEE (Automated Recognition of Bodily Expression of Emotion), has also been developed and evaluated. Our analysis shows the effectiveness of Laban Movement Analysis (LMA) features in characterizing arousal, and our experiments using LMA features further demonstrate computability of bodily expression. We report and compare results of several other baseline methods which were developed for action recognition based on two different modalities, body skeleton, and raw image. The dataset and findings presented in this work will likely serve as a launchpad for future discoveries in body language understanding that will enable future robots to interact and collaborate more effectively with humans.