Facial action coding system
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FOMM learns dense optical flow to support complex motions with fewer artifacts, whereas ICface and FSGAN only use sparse action units or facial landmarks to learn expression and pose, which tend to produce motion artifacts.
“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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- 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
- Social Sciences › Psychology › Experimental and Cognitive Psychology › 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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