The DOI matches its Crossref record; 23 of the 24 references it cites were found in a registry or an index.
Learning Bayesian network classifiers for facial expression recognition both labeled and unlabeled data
Abstract
Understanding human emotions is one of the necessary skills for the computer to interact intelligently with human users. The most expressive way humans display emotions is through facial expressions. In this paper, we report on several advances we have made in building a system for classification of facial expressions from continuous video input. We use Bayesian network classifiers for classifying expressions from video. One of the motivating factor in using the Bayesian network classifiers is their ability to handle missing data, both during inference and training. In particular, we are interested in the problem of learning with both labeled and unlabeled data. We show that when using unlabeled data to learn classifiers, using correct modeling assumptions is critical for achieving improved classification performance. Motivated by this, we introduce a classification driven stochastic structure search algorithm for learning the structure of Bayesian network classifiers. We show that with moderate size labeled training sets and large amount of unlabeled data, our method can utilize unlabeled data to improve classification performance. We also provide results using the Naive Bayes (NB) and the Tree-Augmented Naive Bayes (TAN) classifiers, showing that the two can achieve good performance with labeled training sets, but perform poorly when unlabeled data are added to the training set.
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“Recent work on facial expression analysis has used these “basic expressions” or a subset of them (see Pantic and Rothkrantz’s [19] detailed review of many of the research done in recent years).”
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Metadata
- Authors
- Cohen, I., Sebe, N., Gozman, F. G., Cirelo, M. C., & Huang, T. S.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- Leiden University (NL)
- Universidade de São Paulo (BR)
- ORCID
- Venue
- 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings.
- Publisher
- IEEE Comput. Soc
- Published
- 2003-11-21
- Pages
- I-595-I-601
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2141522145
- Cited by
- 155
- References
- 24
- Citations by year
- 2026: 1 · 2024: 2 · 2023: 4 · 2022: 5 · 2021: 3 · 2020: 7 · 2019: 5 · 2018: 9 · 2017: 5 · 2016: 12 · 2015: 4 · 2014: 10 · 2013: 6 · 2012: 9
- FWCI
- 8.57
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- facial expression recognition, Bayesian network classifiers, semi-supervised learning, unlabeled data, structure learning, missing data, naive Bayes, tree augmented naive Bayes, emotion recognition
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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