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:研究面部表达方式是一项臭名昭著的困难工作。在视觉计算领域最近的进展在自动检测图片和视频的面部表达方式方面取得了令人印象深刻的进展。然而,许多这项工作尚未在心理学等社会科学领域广为传播。目前艺术模型的状况要求大量领域专门知识,而这种专门知识传统上没有纳入社会科学培训方案。此外,明显缺乏方便用户的开放源软件,提供一整套支持面部表达方式研究的工具和功能。在本文中,我们引入了Py-Feat,这是一个开放源码的Python工具箱,为检测、预处理、分析和直观化面部表达数据提供支持。 Py-Feat使域专家易于传播和基准计算机愿景模型,也便于终端用户快速处理、分析和视觉化面部表达数据。我们希望这个平台将促进在人类行为研究中更多地使用面部表达数据。