Estimation of facial action intensities on 2d and 3D data
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Publication in the conference proceedings of EUSIPCO, Barcelona, Spain, 2011
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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 [235]
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 [235]
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 [235]
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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
- Authors
- Bilge, M., Sankur, B., & Savran, A.
- Affiliations
- Boğaziçi University (TR)
- Venue
- Zenodo
- Publisher
- Zenodo
- Published
- 2011-08-29
- Pages
- 1969-1973
- OpenAlex
- W2119640399
- Cited by
- 10
- References
- 0
- Citations by year
- 2025: 1 · 2020: 1 · 2019: 2 · 2016: 1 · 2015: 1 · 2012: 3
- FWCI
- 1.38
- Type
- journal article
- Language
- en
- Open access
- green
- zenodo.org
- Licence
- Locations
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, other-oa)
- Zenodo (CERN European Organization for Nuclear Research) (cc-by)
- 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 expression analysis
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