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Cited by 168Emotion and Mood RecognitionFace recognition and analysisFace and Expression Recognition

The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.

Spontaneous vs. posed facial behavior: automatic analysis of brow actions

Valstar, Michel F., Pantic, Maja, Ambadar, Zara, Cohn, Jeffrey F.·2006·Proceedings of the 8th international conference on Multimodal interfaces
DOI 10.1145/1180995.1181031
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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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Crossref · 10.1145/1180995.1181031

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Research works printed but not linked: 10

  1. Duchenne de Bologne G. B., 1862, Jules Renouard Libraire
  2. Ekman P., 2002, Salt Lake City
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  4. Flecha-Garcia M. L., 2001, Proc. Theoretical & Applied Linguistics -- Postgraduate Conf.
  5. Gu H., 2004, Proc. Int'l Conf. Face & Gesture Recognition, 111--116
  6. 10.5555/795661.796155 · DOI
  7. Kendon A., 1973, Comparative Ecology and Behavior of Primates, Michael
  8. Lucas B., 1981, Proc. Int'l Joint Conf. on Artificial Intelligence, 674--679
  9. 10.5555/1949767.1949787 · DOI
  10. Tipping M. E., 2000, Proc. Int'l Conf. Adv. Neural Inf. Processing Systems, 332--338

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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)
ORCID
Venue
Proceedings of the 8th international conference on Multimodal interfaces
Publisher
ACM
Published
2006-11-02
Pages
162-170
DOI
10.1145/1180995.1181031
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

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