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Facial Action Coding System
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“FOMM learns dense optical low to support complex motions with fewer artifacts, whereas ICface and FSGAN only use sparse action units [13] or facial landmarks to learn the expression and pose, which tend to produce motion artifacts.”
From DEEPFAKER: A Unified Evaluation Platform for Facial Deepfake and Detection Models (2024) · cites this paper as [13]
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Metadata
- Authors
- Ekman, P., & Friesen, W. V.
- Venue
- PsycTESTS Dataset
- Published
- 1978
- OpenAlex
- W1588539311
- Cited by
- 6,019
- References
- 0
- Citations by year
- 2026: 288 · 2025: 447 · 2024: 367 · 2023: 379 · 2022: 314 · 2021: 204 · 2020: 218 · 2019: 195 · 2018: 227 · 2017: 199 · 2016: 244 · 2015: 233 · 2014: 214 · 2013: 208 · 2012: 174
- Type
- dataset
- Language
- en
- Open access
- closed
- Locations
- PsycTESTS Dataset (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Keywords
- facial expressions, facial action units, emotion recognition, facial motion
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