Py-Feat: Python Facial Expression Analysis Toolbox

Author:

Cheong Jin Hyun,Jolly Eshin,Xie Tiankang,Byrne Sophie,Kenney Matthew,Chang Luke J.ORCID

Abstract

AbstractStudying 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.

Funder

National Institute of Mental Health

National Science Foundation

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences,General Environmental Science

Reference107 articles.

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2. Generative Conditional Facial Reenactment Method using a Human Expression Palette;2023 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE);2023-09-24

3. Multimodal Emotion Classification Supported in the Aggregation of Pre-trained Classification Models;Computational Science – ICCS 2023;2023

4. Facial Action Unit-Based Deepfake Video Detection Using Deep Learning;2022 4th International Conference on Current Research in Engineering and Science Applications (ICCRESA);2022-12-20

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