Artificial Intelligence Facial Expression Recognition for Emotion Detection: Performance and Acceptance
Document Type
Article
Publication Date
2020
Publication Title
Issues in Information Systems
Abstract
The understanding of emotions has been an important research topic for many years dating back to almost 150 years to the work of Charles Darwin. Human emotion detection and recognition play an important role in everyday life as a fundamental part in interpersonal communication. Naturally, the relationship between detection and recognition is of high importance in the human computer interaction. Facial expression recognition (FER) systems are a computerbased technology that detects faces, codes expressions, and determines emotional states. This paper discusses the creation and reports the testing of a FER system to determine if machines could more accurately predict emotion than humans. In addition, this study seeks to determine perception of artificial intelligence facial expression recognition. The methods in this study included using a Haar-cascade classifier implemented by OpenCV 2.4.2. Results demonstrated that the AI FER system recognized emotions more accurately and that the human subjects considered the Artificial Intelligence Facial Expression Recognition systems trustworthy and beneficial.
Recommended Citation
George, Annu, Hayden Wimmer, Carl Rebman.
2020.
"Artificial Intelligence Facial Expression Recognition for Emotion Detection: Performance and Acceptance."
Issues in Information Systems, 21 (4): 81-91: International Association for Computer Information Systems.
source: http://www.iacis.org/iis/2020/4_iis_2020_81-91.pdf
https://digitalcommons.georgiasouthern.edu/information-tech-facpubs/102
Copyright
© Copyright, IACIS 2020
