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The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression
Abstract
In 2000, the Cohn-Kanade (CK) database was released for the purpose of promoting research into automatically detecting individual facial expressions. Since then, the CK database has become one of the most widely used test-beds for algorithm development and evaluation. During this period, three limitations have become apparent: 1) While AU codes are well validated, emotion labels are not, as they refer to what was requested rather than what was actually performed, 2) The lack of a common performance metric against which to evaluate new algorithms, and 3) Standard protocols for common databases have not emerged. As a consequence, the CK database has been used for both AU and emotion detection (even though labels for the latter have not been validated), comparison with benchmark algorithms is missing, and use of random subsets of the original database makes meta-analyses difficult. To address these and other concerns, we present the Extended Cohn-Kanade (CK+) database. The number of sequences is increased by 22% and the number of subjects by 27%. The target expression for each sequence is fully FACS coded and emotion labels have been revised and validated. In addition to this, non-posed sequences for several types of smiles and their associated metadata have been added. We present baseline results using Active Appearance Models (AAMs) and a linear support vector machine (SVM) classifier using a leave-one-out subject cross-validation for both AU and emotion detection for the posed data. The emotion and AU labels, along with the extended image data and tracked landmarks will be made available July 2010.
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Metadata
- Authors
- Lucey, P., Cohn, J. F., Kanade, T., Saragih, J., Ambadar, Z., & Matthews, I.
- Affiliations
- University of Pittsburgh (US)
- Carnegie Mellon University (US)
- Walt Disney (United States) (US)
- Venue
- 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops
- Publisher
- IEEE
- Published
- 2010-06
- Pages
- 94-101
- ISSN
- 2160-7508 (ISSN-L)
- OpenAlex
- W2103943262
- Cited by
- 4,303
- References
- 29
- Citations by year
- 2026: 139 · 2025: 286 · 2024: 350 · 2023: 462 · 2022: 440 · 2021: 464 · 2020: 411 · 2019: 397 · 2018: 332 · 2017: 268 · 2016: 214 · 2015: 199 · 2014: 139 · 2013: 110 · 2012: 53
- FWCI
- 121.79
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (publishedVersion)
- Funders
- National Institutes of Health
- National Institute of Mental Health
- 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
- facial expression recognition, facial action units, emotion recognition, facial landmark tracking, active appearance models, support vector classifier, benchmark dataset
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.
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