Paper Info
Title
Improving Human-Robot Interaction by Enhancing NAO Robot Awareness of Human Facial Expression
Abstract
An intriguing challenge in the human-robot interaction field is the prospect of endowing robots with emotional intelligence to make the interaction more genuine, intuitive, and natural. A crucial aspect in achieving this goal is the robot's capability to infer and interpret human emotions. Thanks to its design and open programming platform, the NAO humanoid robot is one of the most widely used agents for human interaction. As with person-to-person communication, facial expressions are the privileged channel for recognizing the interlocutor's emotional expressions. Although NAO is equipped with a facial expression recognition module, specific use cases may require additional features and affective computing capabilities that are not currently available. This study proposes a highly accurate convolutional-neural-network-based facial expression recognition model that is able to further enhance the NAO robot' awareness of human facial expressions and provide the robot with an interlocutor's arousal level detection capability. Indeed, the model tested during human-robot interactions was 91% and 90% accurate in recognizing happy and sad facial expressions, respectively; 75% accurate in recognizing surprised and scared expressions; and less accurate in recognizing neutral and angry expressions. Finally, the model was successfully integrated into the NAO SDK, thus allowing for high-performing facial expression classification with an inference time of 0.34 & PLUSMN; 0.04 s.
Year
DOI
Venue
2021
10.3390/s21196438
SENSORS
Keywords
DocType
Volume
facial expression recognition, emotion recognition, human-robot interaction, affective computing, machine learning
Journal
21
Issue
ISSN
Citations 
19
1424-8220
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Chiara Filippini100.68
David Perpetuini200.68
Daniela Cardone3122.80
Arcangelo Merla4213.56