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
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398
Ekman, P., & Friesen, W. V. (2019) · PsycTESTS Dataset · doi:10.1037/t27734-000 · Cited by 6,019
Cootes, T. F., Edwards, G. J., & Taylor, C. J. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.927467 · Cited by 5,409
Ahmed, N., Natarajan, T., & Rao, K. R. (1974) · IEEE Transactions on Computers · doi:10.1109/t-c.1974.223784 · Cited by 5,156
The JPEG still picture compression standard
Wallace, G. K. (1991) · Communications of the ACM · doi:10.1145/103085.103089 · Cited by 3,804
The Behavioral and Brain Sciences
D., C. P. (1979) · The American Journal of Psychology · doi:10.2307/1421932 · Cited by 2,946
Facial expression recognition based on Local Binary Patterns: A comprehensive study
Shan, C., Gong, S., & McOwan, P. W. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2008.08.005 · Cited by 2,300
Painful data: The UNBC-McMaster shoulder pain expression archive database
Lucey, P., Cohn, J. F., Prkachin, K. M., Solomon, P. E., & Matthews, I. (2011) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2011.5771462 · Cited by 634
Facial expression of pain: An evolutionary account
Williams, A. C. D. C. (2002) · Behavioral and Brain Sciences · doi:10.1017/s0140525x02000080 · Cited by 612
Prkachin, K. M., & Solomon, P. E. (2008) · Pain · doi:10.1016/j.pain.2008.04.010 · Cited by 472
Automatic coding of facial expressions displayed during posed and genuine pain
Littlewort, G. C., Bartlett, M. S., & Lee, K. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2008.12.010 · Cited by 216
Lucey, P., Cohn, J. F., Prkachin, K. M., Solomon, P. E., Chew, S. W., & Matthews, I. (2011) · Image and Vision Computing · doi:10.1016/j.imavis.2011.12.003 · Cited by 171
Machine recognition and representation of neonatal facial displays of acute pain
Brahnam, S., Chuang, C.-F., Shih, F. Y., & Slack, M. R. (2005) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2004.12.003 · Cited by 145
Real-time assessment of pain behavior during clinical assessment of low back pain patients
Prkachin, K. M., Hughes, E., Schultz, I. Z., Joy, P. W., & Hunt, D. G. (2002) · Pain · doi:10.1016/s0304-3959(01)00369-4 · Cited by 60
Agitation and pain assessment using digital imaging
Gholami, B., Haddad, W. M., & Tannenbaum, A. (2009) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2009.5332437 · Cited by 39
Shape-constrained Gaussian process regression for facial-point-based head-pose normalization
Rudovic, O. O., & Pantić, M. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126407 · Cited by 19
Self-citation
Research works printed but not linked: 1
- Tipping, M.E.: Sparse Bayesian learning and the relevance vector machine. The Journal of Machine Learning Research 1, 211–244 (2001)
Cited by
Sorted by citations
Automatic Analysis of Facial Affect: A Survey of Registration, Representation, and Recognition
Sarıyanidi, E., Güneş, H., & Cavallaro, A. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2366127 · Cited by 702
Corneanu, C. A., Simon, M. O., Cohn, J. F., & Guerrero, S. E. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2515606 · Cited by 527
Automatic Analysis of Facial Actions: A Survey
Martínez, B., Valstar, M., Jiang, B., & Pantić, M. (2017) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2731763 · Cited by 370
Robust Lightweight Facial Expression Recognition Network with Label Distribution Training
Zhao, Z., Liu, Q., & Zhou, F. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i4.16465 · Cited by 307
Deep Pain: Exploiting Long Short-Term Memory Networks for Facial Expression Classification
Rodriguez, P., Cucurull, G., Gonzalez, J., Gonfaus, J. M., Nasrollahi, K., Moeslund, T. B., & Roca, F. X. (2022) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2662199 · Cited by 297
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
