Survey on Emotional Body Gesture Recognition

TL;DR

This survey reviews the current state of automatic emotional body gesture recognition, highlighting challenges such as data scarcity and shallow representations, and discusses recent methods and multi-modal approaches to improve accuracy.

Contribution

It provides a comprehensive framework for understanding and advancing emotional body gesture recognition, integrating recent literature, and identifying key challenges and future directions.

Findings

Pre-processing methods are mature for large-scale analysis.

Data scarcity hampers emotion recognition accuracy.

Representations are shallow and lack standardization.

Abstract

Automatic emotion recognition has become a trending research topic in the past decade. While works based on facial expressions or speech abound, recognizing affect from body gestures remains a less explored topic. We present a new comprehensive survey hoping to boost research in the field. We first introduce emotional body gestures as a component of what is commonly known as "body language" and comment general aspects as gender differences and culture dependence. We then define a complete framework for automatic emotional body gesture recognition. We introduce person detection and comment static and dynamic body pose estimation methods both in RGB and 3D. We then comment the recent literature related to representation learning and emotion recognition from images of emotionally expressive gestures. We also discuss multi-modal approaches that combine speech or face with body gestures for improved emotion recognition. While pre-processing methodologies (e.g. human detection and pose estimation) are nowadays mature technologies fully developed for robust large scale analysis, we show that for emotion recognition the quantity of labelled data is scarce, there is no agreement on clearly defined output spaces and the representations are shallow and largely based on naive geometrical representations.