Regression-based intensity estimation of facial action units
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A number of studies question the validity of estimating intensity from distance to the classification hyperplane.
“A number of studies question the validity of estimating intensity from distance to the classification hyperplane [235], [236], [237].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [236]
Correlations on upper face are significantly higher than the lower face for both modalities.
“Nevertheless, correlations on upper face are significantly higher than the lower face for both modalities.”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [236]
Estimating facial AUs intensity followed a few distinct approaches: critical analysis of limitations of estimating intensity from classification scores, direct estimation from features, studies on optimal representations, and modelling correlations between AUs appearance and intensity priors.
“In summary, estimating facial AUs intensity followed a few distinct approaches. First, some researchers made a critical analysis about the limitations of estimating intensity from classification scores [235], [236], [237]. As an alternative, direct estimation from features was analyzed. Further studies on optimal representations for intensity estimation of different AUs were published either from”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [236]
RGB representation performs better on the upper face while 3D representation performs better on the lower face, and there is an overall improvement if RGB and 3D intensity estimations are fused.
“When evaluated comparatively, RGB representation performs better on the upper face while 3D representation performs better on the lower face and there is an overall improvement if RGB and 3D intensity estimations are fused.”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [236]
In recent years, a critical analysis has been made about the limitations of estimating action unit intensity from classification scores, and estimation directly from features was analysed.
“In recent years, a critical analysis has been made about the limitations of estimating AUs intensity from classification scores [235], [236], [237] and estimation directly from features were analysed.”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [236]
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Cites
3D facial expression recognition based on automatically selected features
Tang, H., & Huang, T. S. (2008) · 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2008.4563052 · Cited by 140 · see citing works
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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 · see citing works
FERA 2015 - second Facial Expression Recognition and Analysis challenge
Valstar, M. F., Almaev, T., Girard, J. M., McKeown, G., Mehu, M., Yin, L., Pantic, M., & Cohn, J. F. (2015) · 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG) · doi:10.1109/fg.2015.7284874 · Cited by 252 · see citing works
Temporal Bayesian Fusion for Affect Sensing: Combining Video, Audio, and Lexical Modalities
Savran, A., Cao, H., Nenkova, A., & Verma, R. (2015) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2014.2362101 · Cited by 53 · see citing works
Self-citationEstimating smile intensity: A better way
Girard, J. M., Cohn, J. F., & De la Torre, F. (2015) · Pattern Recognition Letters · doi:10.1016/j.patrec.2014.10.004 · Cited by 44 · see citing works
Zhao, X., Dellandréa, E., Zou, J., & Chen, L. (2013) · Image and Vision Computing · doi:10.1016/j.imavis.2012.10.001 · Cited by 27 · see citing works
- Authors
- Savran, A., Sankur, B., & Taha Bilge, M.
- Affiliations
- Boğaziçi University (TR)
- Corresponding author
- Savran, Arman
- ORCID
- Savran, Arman
- Venue
- Image and Vision Computing
- Publisher
- Elsevier BV
- Published
- 2012-10
- Volume
- 30
- Issue
- 10
- Pages
- 774-784
- ISSN
- 0262-8856 (ISSN-L)
- OpenAlex
- W2015212590
- Cited by
- 129
- References
- 1
- Citations by year
- 2025: 2 · 2024: 3 · 2023: 2 · 2022: 7 · 2021: 10 · 2020: 5 · 2019: 10 · 2018: 10 · 2017: 15 · 2016: 14 · 2015: 19 · 2014: 16 · 2013: 11 · 2012: 5
- FWCI
- 11.03
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Image and Vision Computing (publishedVersion)
- Funders
- Türkiye Bilimsel ve Teknolojik Araştırma Kurumu
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- facial action units, facial expressions, facial action unit detection, regression estimation, feature selection, mean curvature, Gaussian curvature, shape index, curvedness, multimodal fusion
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