  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.

Computer Vision and Pattern Recognition;Machine Learning;Image and Video Processing