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

IF 3.5 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Multivariate Behavioral Research Pub Date : 2025-05-01 Epub Date: 2025-02-14 DOI:10.1080/00273171.2025.2455497
Austin Wyman, Zhiyong Zhang
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Abstract

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.

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在R语言中使用人工智能工具进行面部情感识别的教程。
几十年来,面部情绪的自动检测一直是社会和行为研究中的一个有趣话题,但直到最近才成为可能。在本教程中,我们回顾了三个流行的基于人工智能的情感检测程序,它们是R程序员可以访问的:谷歌Cloud Vision, Amazon Rekognition和Py-Feat。我们介绍了它们的优点和缺点,并提供了示例代码,以便研究人员可以立即开始设计,收集和分析情感数据。此外,我们提供了机器学习、深度学习和计算机视觉算法的入门级解释,这些算法是大多数情感检测程序的基础,以提高社会和行为科学文献中可解释的人工智能的素养。
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来源期刊
Multivariate Behavioral Research
Multivariate Behavioral Research 数学-数学跨学科应用
CiteScore
7.60
自引率
2.60%
发文量
49
审稿时长
>12 weeks
期刊介绍: Multivariate Behavioral Research (MBR) publishes a variety of substantive, methodological, and theoretical articles in all areas of the social and behavioral sciences. Most MBR articles fall into one of two categories. Substantive articles report on applications of sophisticated multivariate research methods to study topics of substantive interest in personality, health, intelligence, industrial/organizational, and other behavioral science areas. Methodological articles present and/or evaluate new developments in multivariate methods, or address methodological issues in current research. We also encourage submission of integrative articles related to pedagogy involving multivariate research methods, and to historical treatments of interest and relevance to multivariate research methods.
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