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

Datos Bibliográficos

ID19291237
AutoresAustin Wyman (0000-0001-6060-0583, University of Notre Dame, autor de correspondencia), Zhiyong Zhang (0000-0003-1622-3447, University of Notre Dame)
Año2025
Volumen60
Número3
Páginas641-655
Fecha de publicación2025-05-04
Open AccessSí
TipoARTICLE
RevistaMultivariate Behavioral Research (JOURNAL)
Identificadores de la revistaISSN: 0027-3171 • E-ISSN: 1532-7906
EditorialInforma UK Limited (PUBLISHER • GB)
DOI10.1080/00273171.2025.2455497
PMID39949325
OpenAlexW4407586635
IdiomaEN
Referencias 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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