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

Austin Wyman¹, Zhiyong Zhang¹

¹Department of Psychology, University of Notre Dame, Notre Dame, IN, USA.

Multivariate Behavioral Research

|February 14, 2025

Summary

This summary is machine-generated.

This tutorial reviews three AI-powered facial emotion detection tools for R programmers: Google Cloud Vision, Amazon Rekognition, and Py-Feat. It offers practical guidance and explains underlying machine learning for social science research.

Keywords:

Amazon Rekognition Emotion recognition Google Cloud Vision Py-Feat artificial intelligence emotion detection

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Area of Science:

Social and Behavioral Sciences
Computer Science
Artificial Intelligence

Background:

Automated facial emotion detection has been a long-standing interest in social and behavioral research.
Recent advancements in artificial intelligence (AI) have made automated emotion detection feasible.

Purpose of the Study:

To review three popular AI-based emotion detection programs accessible to R programmers.
To provide researchers with practical guidance and sample code for emotion data analysis.
To enhance understanding of the machine learning algorithms behind emotion detection.

Main Methods:

Review of Google Cloud Vision, Amazon Rekognition, and Py-Feat.
Presentation of advantages, disadvantages, and sample R code.
Introductory explanation of machine learning, deep learning, and computer vision algorithms.

Main Results:

Identified key features and limitations of each reviewed AI tool.
Provided functional code examples for immediate use by researchers.
Offered foundational knowledge of AI algorithms for improved explainability.

Conclusions:

Accessible AI tools can empower social and behavioral scientists to conduct emotion detection research.
Understanding the underlying AI technology is crucial for responsible and effective application.
This tutorial facilitates the integration of AI into emotion data collection and analysis.