A Tutorial on the Use of Artificial Intelligence Tools for Facial Emotion Recognition in R

Dados Bibliográficos

ID19291237
AutoresAustin Wyman (0000-0001-6060-0583, University of Notre Dame, autor correspondente), Zhiyong Zhang (0000-0003-1622-3447, University of Notre Dame)
Ano2025
Volume60
Fascículo3
Páginas641-655
Data de publicação2025-05-04
Open AccessSim
TipoARTICLE
PeriódicoMultivariate Behavioral Research (JOURNAL)
Identificadores do periódicoISSN: 0027-3171 • E-ISSN: 1532-7906
EditoraInforma UK Limited (PUBLISHER • GB)
DOI10.1080/00273171.2025.2455497
PMID39949325
OpenAlexW4407586635
IdiomaEN
Referências citadas47

Automated detection of facial emotions has been an interesting topic for multiple decades in social and behavioral research but is only possible very recently. In this tutorial, we review three popular artificial intelligence based emotion detection programs that are accessible to R programmers: Google Cloud Vision, Amazon Rekognition, and Py-Feat. We present their advantages, disadvantages, and provide sample code so that researchers can immediately begin designing, collecting, and analyzing emotion data. Furthermore, we provide an introductory level explanation of the machine learning, deep learning, and computer vision algorithms that underlie most emotion detection programs in order to improve literacy of explainable artificial intelligence in the social and behavioral science literature

Emotion recognition · Facial expression · Facial recognition system · Machine learning · Natural language processing · Pattern recognition (psychology) · Artificial Intelligence · Computer Science · Emotion and Mood Recognition · Face and Expression Recognition · Face recognition and analysis · Psychology

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