Facial emotion expressions in human-robot interaction: A survey

TL;DR

This survey reviews current methods and challenges in recognizing and generating facial emotion expressions in human-robot interaction, highlighting high accuracy on datasets but lower real-time performance and limited automatic robot expression capabilities.

Contribution

It provides a comprehensive overview of existing techniques in facial emotion recognition and robotic expression generation, identifying gaps and future research directions.

Findings

High accuracy on predefined datasets

Lower accuracy in real-time recognition

Limited automatic facial expression generation in robots

Abstract

Facial expressions are an ideal means of communicating one's emotions or intentions to others. This overview will focus on human facial expression recognition as well as robotic facial expression generation. In the case of human facial expression recognition, both facial expression recognition on predefined datasets as well as in real-time will be covered. For robotic facial expression generation, hand-coded and automated methods i.e., facial expressions of a robot are generated by moving the features (eyes, mouth) of the robot by hand-coding or automatically using machine learning techniques, will also be covered. There are already plenty of studies that achieve high accuracy for emotion expression recognition on predefined datasets, but the accuracy for facial expression recognition in real-time is comparatively lower. In the case of expression generation in robots, while most of the robots are capable of making basic facial expressions, there are not many studies that enable robots to do so automatically. In this overview, state-of-the-art research in facial emotion expressions during human-robot interaction has been discussed leading to several possible directions for future research.