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.

翻译：研究面部表情是一项众所周知的艰巨任务。情感计算领域的最新进展已在从图片和视频中自动检测面部表情方面取得了令人瞩目的进步。然而，这些成果中的大部分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相当多的领域专业知识，而这些知识传统上并未纳入社会科学培训课程。此外，目前明显缺乏一套提供全面工具和功能以支持面部表情研究的用户友好型开源软件。本文介绍了Py-Feat，一个开源的Python工具箱，支持面部表情数据的检测、预处理、分析和可视化。Py-Feat使领域专家能够轻松传播和评估计算机视觉模型，同时也便于最终用户快速处理、分析和可视化面部表情数据。我们希望这个平台能够促进面部表情数据在人类行为研究中的更广泛应用。