Estimating smile intensity: A better way
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Abstract
Both the occurrence and intensity of facial expressions are critical to what the face reveals. While much progress has been made towards the automatic detection of facial expression occurrence, controversy exists about how to estimate expression intensity. The most straight-forward approach is to train multiclass or regression models using intensity ground truth. However, collecting intensity ground truth is even more time consuming and expensive than collecting binary ground truth. As a shortcut, some researchers have proposed using the decision values of binary-trained maximum margin classifiers as a proxy for expression intensity. We provide empirical evidence that this heuristic is flawed in practice as well as in theory. Unfortunately, there are no shortcuts when it comes to estimating smile intensity: researchers must take the time to collect and train on intensity ground truth. However, if they do so, high reliability with expert human coders can be achieved. Intensity-trained multiclass and regression models outperformed binary-trained classifier decision values on smile intensity estimation across multiple databases and methods for feature extraction and dimensionality reduction. Multiclass models even outperformed binary-trained classifiers on smile occurrence detection.
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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 [237]
Intensity-trained multiclass and regression models outperformed binary-trained classifier decision values on smile intensity estimation across multiple databases and methods.
“More recently, [237] found that intensity-trained multiclass and regression models outperformed binary-trained classifier decision values on smile intensity estimation across multiple databases and methods for feature extraction and dimensionality reduction.”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [237]
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 [237]
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 [237]
Research suggests that using classifier scores for predicting intensity is conceptually wrong and that intensity levels should be directly learned from the ground truth.
“Research suggests that using classifier scores for predicting intensity is conceptually wrong and that intensity levels should be directly learned from the ground truth [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 [237]
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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
Self-citation
- Authors
- Girard, J. M., Cohn, J. F., & De la Torre, F.
- Affiliations
- University of Pittsburgh (US)
- Corresponding author
- Girard, Jeffrey M.
- Venue
- Pattern Recognition Letters
- Publisher
- Elsevier BV
- Published
- 2015-11
- Volume
- 66
- Pages
- 13-21
- ISSN
- 0167-8655 (ISSN-L)
- PMC
- PMC4598946
- OpenAlex
- W1994738089
- Cited by
- 44
- References
- 97
- Citations by year
- 2025: 1 · 2024: 1 · 2022: 3 · 2021: 8 · 2020: 5 · 2019: 6 · 2018: 2 · 2017: 8 · 2016: 6 · 2015: 4
- FWCI
- 8.35
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Pattern Recognition Letters (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation
- National Institutes of Health
- National Institute of Mental Health (R01MH096951)
- 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
- facial expression intensity, maximum margin classifier, feature extraction, dimensionality reduction
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 ›
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