Py-Feat: Python Facial Expression Analysis Toolbox

IF 2.1 Q2 PSYCHOLOGY Affective science Pub Date : 2023-08-08 DOI:10.1007/s42761-023-00191-4
Jin Hyun Cheong, Eshin Jolly, Tiankang Xie, Sophie Byrne, Matthew Kenney, Luke J. Chang
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Abstract

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.

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Py-Feat:Python 面部表情分析工具箱
研究面部表情是一项出了名的困难工作。情感计算领域的最新进展在自动检测图片和视频中的面部表情方面取得了令人瞩目的进展。然而,这项工作的大部分成果尚未在心理学等社会科学领域得到广泛传播。当前最先进的模型需要大量的专业领域知识,而这些知识传统上并没有纳入社会科学培训计划。此外,目前明显缺乏用户友好的开源软件,来提供一整套支持面部表情研究的工具和功能。在本文中,我们将介绍 Py-Feat,这是一个开源 Python 工具箱,可为面部表情数据的检测、预处理、分析和可视化提供支持。Py-Feat 可使领域专家轻松传播计算机视觉模型并对其进行基准测试,也可使终端用户快速处理、分析和可视化面部表情数据。我们希望这个平台能促进在人类行为研究中更多地使用面部表情数据:在线版本包含补充材料,可查阅 10.1007/s42761-023-00191-4。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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Introduction to the Special Section Commentaries Affectivism and the Emotional Elephant: How a Componential Approach Can Reconcile Opposing Theories to Serve the Future of Affective Sciences A Developmental Psychobiologist’s Commentary on the Future of Affective Science Emotional Overshadowing: Pleasant and Unpleasant Cues Overshadow Neutral Cues in Human Associative Learning Emphasizing the Social in Social Emotion Regulation: A Call for Integration and Expansion
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