Continuous Pain Intensity Estimation from Facial Expressions
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Pain detection is used for monitoring patient progress in clinical settings.
“Pain detection is used for monitoring patient progress in clinical settings [48], [49], [50].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [49]
Appearance features achieve better results than shape features for continuous AU intensity estimation.
“A first conclusion is that appearance features achieve better results than shape features.”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [49]
The approach in [49] is static, which would fail to distinguish between eye blink and eye closure, and does not exploit correlations between apparitions of different AUs.
“On the other hand this approach is static, which would fail to distinguish between eye blink and eye closure, and does not exploit the correlations between apparitions of different AUs.”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [49]
The fusion between DCT and LBP gives the best performance for continuous AU intensity estimation.
“Even more, the fusion between the two appearance representations, DCT and LBP, gives the best performance even though a proper alignment might improve the contribution of the shape representation as well.”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [49]
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- Authors
- Kaltwang, S., Rudovic, O., & Pantic, M.
- Affiliations
- Imperial College London (GB)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2012
- Pages
- 368-377
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2098615198
- Cited by
- 245
- References
- 17
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 9 · 2023: 6 · 2022: 9 · 2021: 22 · 2020: 21 · 2019: 27 · 2018: 27 · 2017: 26 · 2016: 37 · 2015: 24 · 2014: 17 · 2013: 9
- FWCI
- 29.93
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- University of Twente Research Information (publishedVersion)
- University of Twente Research Information (submittedVersion)
- Funders
- Engineering and Physical Sciences Research Council (EP/H016988/1)
- 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
- pain intensity estimation, facial expressions, facial landmarks, DCT features, local binary pattern, regression analysis, shoulder pain
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