The DOI matches its Crossref record; 16 of the 17 references it cites were found in a registry or an index.
Continuous Pain Intensity Estimation from Facial Expressions
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In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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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Citations
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Heteroscedastic Conditional Ordinal Random Fields for Pain Intensity Estimation from Facial ImagesPreprint
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Self-citationFacial Action Recognition in 2D and 3D
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A Novel Approach for Pain Intensity Detection by KAZE Features
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Metadata
- 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
- Physical Sciences › Computer Science › Artificial Intelligence › 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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