Automatic Recognition of Facial Actions in Spontaneous Expressions
Abstract
Abstract — Spontaneous facial expressions differ from posed expressions in both which muscles are moved, and in the dynamics of the movement. Advances in the field of automatic facial expression measurement will require development and assessment on spontaneous behavior. Here we present preliminary results on a task of facial action detection in spontaneous facial expressions. We employ a user independent fully automatic system for real time recognition of facial actions from the Facial Action Coding System (FACS). The system automatically detects frontal faces in the video stream and coded each frame with respect to 20 Action units. The approach applies machine learning methods such as support vector machines and AdaBoost, to texture-based image representations. The output margin for the learned classifiers predicts action unit intensity. Frame-by-frame intensity measurements will enable investigations into facial expression dynamics which were previously intractable by human coding. I.
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The first seminal work observed a correlation between a classifier's output margin (distance to SVM hyperplane) and the intensity of the facial expression, but only weakly for spontaneous FEs.
“It observed a correlation between a classifier’s output margin, in this case the distance to the hyperplane of a SVM classification, and the intensity of the facial expression. Unfortunately this was only weakly observered for spontaneous FEs.”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [234]
The first seminal work on intensity estimation was published in 2006.
“Even though attempts in estimating FE intensity have existed before [233], the first seminal work was published in 2006 [234].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [234]
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References 45 · Cited by 468
- Authors
- Bartlett, M. S., Littlewort, G. C., Frank, M. G., Lainscsek, C., Fasel, I. R., & Movellan, J. R.
- Affiliations
- University of California San Diego (US)
- University at Buffalo, State University of New York (US)
- Venue
- Journal of Multimedia
- Publisher
- Academy Publisher
- Published
- 2006-09-01
- Volume
- 1
- Issue
- 6
- ISSN
- 1796-2048 (ISSN-L)
- OpenAlex
- W2159668072
- Cited by
- 468
- References
- 45
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 7 · 2023: 17 · 2022: 17 · 2021: 13 · 2020: 19 · 2019: 21 · 2018: 25 · 2017: 33 · 2016: 23 · 2015: 37 · 2014: 46 · 2013: 45 · 2012: 34
- FWCI
- 22.75
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Multimedia (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
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- facial action units, spontaneous facial expressions, facial expression recognition, facial action coding system, frontal face detection, real-time video analysis, support vector machine, AdaBoost, facial expression dynamics
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