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Spontaneous vs. posed facial behavior: automatic analysis of brow actions
Abstract
Past research on automatic facial expression analysis has focused mostly on the recognition of prototypic expressions of discrete emotions rather than on the analysis of dynamic changes over time, although the importance of temporal dynamics of facial expressions for interpretation of the observed facial behavior has been acknowledged for over 20 years. For instance, it has been shown that the temporal dynamics of spontaneous and volitional smiles are fundamentally different from each other. In this work, we argue that the same holds for the temporal dynamics of brow actions and show that velocity, duration, and order of occurrence of brow actions are highly relevant parameters for distinguishing posed from spontaneous brow actions. The proposed system for discrimination between volitional and spontaneous brow actions is based on automatic detection of Action Units (AUs) and their temporal segments (onset, apex, offset) produced by movements of the eyebrows. For each temporal segment of an activated AU, we compute a number of mid-level feature parameters including the maximal intensity, duration, and order of occurrence. We use Gentle Boost to select the most important of these parameters. The selected parameters are used further to train Relevance Vector Machines to determine per temporal segment of an activated AU whether the action was displayed spontaneously or volitionally. Finally, a probabilistic decision function determines the class (spontaneous or posed) for the entire brow action. When tested on 189 samples taken from three different sets of spontaneous and volitional facial data, we attain a 90.7% correct recognition rate.
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Cites
(1981) · Cited by 11,963
Friedman, J., Hastie, T., & Tibshirani, R. (2000) · The Annals of Statistics · doi:10.1214/aos/1016218223 · Cited by 6,884
Ekman, P., & Friesen, W. V. (2019) · PsycTESTS Dataset · doi:10.1037/t27734-000 · Cited by 6,019
Comprehensive database for facial expression analysis
Takeo Kanade, A., Cohn, J. F., & Tian, Y. (2002) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.2000.840611 · Cited by 2,644
Self-citationThin slices of expressive behavior as predictors of interpersonal consequences: A meta-analysis.
Ambady, N., & Rosenthal, R. (1992) · Psychological Bulletin · doi:10.1037/0033-2909.111.2.256 · Cited by 2,003
Ekman, P., & Rosenberg, E. L. (2005) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195179644.001.0001 · Cited by 1,971
Li, S. Z., Jain, A. K., Deng, J., & service), S. (. (2011) · Springer eBooks · doi:10.1007/978-0-85729-932-1 · Cited by 1,867
Web-Based Database for Facial Expression Analysis
Pantić, M., Valstar, M., Rademaker, R. M., & Maat, L. (2005) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2005.1521424 · Cited by 1,124
Self-citationFelt, false, and miserable smiles
Ekman, P., & Friesen, W. V. (1982) · Journal of Nonverbal Behavior · doi:10.1007/bf00987191 · Cited by 1,074
Tipping, M. E. (1999) · Cited by 869
Research works printed but not linked: 10
- Duchenne de Bologne G. B., 1862, Jules Renouard Libraire
- Ekman P., 2002, Salt Lake City
- 10.5555/1032641.1033044 · DOI
- Flecha-Garcia M. L., 2001, Proc. Theoretical & Applied Linguistics -- Postgraduate Conf.
- Gu H., 2004, Proc. Int'l Conf. Face & Gesture Recognition, 111--116
- 10.5555/795661.796155 · DOI
- Kendon A., 1973, Comparative Ecology and Behavior of Primates, Michael
- Lucas B., 1981, Proc. Int'l Joint Conf. on Artificial Intelligence, 674--679
- 10.5555/1949767.1949787 · DOI
- Tipping M. E., 2000, Proc. Int'l Conf. Adv. Neural Inf. Processing Systems, 332--338
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Dybała, P., Ptaszynski, M. E., Rzepka, R., & Araki, K. (2010) · International Journal of Artificial Intelligence Tools · doi:10.1142/s0218213010000431 · Cited by 10
Metadata
- Authors
- Valstar, M. F., Pantic, M., Ambadar, Z., & Cohn, J. F.
- Affiliations
- Imperial College London (GB)
- Imperial College London, UK and University of Twente, The Netherlands
- University of Pittsburgh (US)
- Venue
- Proceedings of the 8th international conference on Multimodal interfaces
- Publisher
- ACM
- Published
- 2006-11-02
- Pages
- 162-170
- OpenAlex
- W2131462465
- Cited by
- 168
- References
- 49
- Citations by year
- 2026: 1 · 2024: 3 · 2022: 4 · 2021: 8 · 2020: 5 · 2019: 8 · 2018: 9 · 2017: 15 · 2016: 13 · 2015: 14 · 2014: 13 · 2013: 14 · 2012: 6
- FWCI
- 23.32
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION (publishedVersion)
- University of Twente Research Information (publishedVersion)
- Spiral (Imperial College London) (submittedVersion)
- University of Twente Research Information (submittedVersion)
- Funders
- 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 action units, eyebrow movements, facial expression analysis, relevance vector machine, mid-level features
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.
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