The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Py-Feat: Python Facial Expression Analysis Toolbox
Open accessAbstract
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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Cites
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AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild
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Cited by
Synchronized affect in shared experiences strengthens social connection
Cheong, J. H., Molani, Z., Sadhukha, S., & Chang, L. J. (2023) · Communications Biology · doi:10.1038/s42003-023-05461-2 · Cited by 75
Self-citationSong, X., Zhang, Y., Lu, Z., Xu, L., & Shen, H. (2026) · Computers & Education · doi:10.1016/j.compedu.2026.105578 · Cited by 12
Metadata
- Authors
- Cheong, J. H., Jolly, E., Xie, T., Byrne, S., Kenney, M., & Chang, L. J.
- Venue
- Affective Science
- Published
- 2023-08-08
- Volume
- 4
- Issue
- 4
- Pages
- 781-796
- ISSN
- 2662-2041 (ISSN-L)
- 2662-205X
- OpenAlex
- W3142113686
- Cited by
- 178
- References
- 15
- FWCI
- 36.69
- Type
- journal article
- Language
- en
- Open access
- hybrid
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