Py-Feat: Python Facial Expression Analysis Toolbox

Studying facial expressions is a notoriously difficult endeavor. Recent advances in the field of affective computing have yielded impressive progress in automatically detecting facial expressions from pictures and videos. However, much of this work has yet to be widely disseminated in social science...

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Bibliographic Details
Published inarXiv.org
Main Authors Cheong, Jin Hyun, Jolly, Eshin, Xie, Tiankang, Byrne, Sophie, Kenney, Matthew, Chang, Luke J
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 07.03.2023
Subjects
Affective computing
Computer vision
Domains
End users
Psychology
Social sciences
Source code
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Summary:Studying facial expressions is a notoriously difficult endeavor. Recent advances in the field of affective computing have yielded impressive progress in automatically detecting facial expressions from pictures and videos. However, much of this work has yet to be widely disseminated in social science domains such as psychology. Current state of the art models require considerable domain expertise that is not traditionally incorporated into social science training programs. Furthermore, there is a notable absence of user-friendly and open-source software that provides a comprehensive set of tools and functions that support facial expression research. In this paper, we introduce Py-Feat, an open-source Python toolbox that provides support for detecting, preprocessing, analyzing, and visualizing facial expression data. Py-Feat makes it easy for domain experts to disseminate and benchmark computer vision models and also for end users to quickly process, analyze, and visualize face expression data. We hope this platform will facilitate increased use of facial expression data in human behavior research.
ISSN:2331-8422