Recognising spontaneous facial micro-expressions
Abstract
Facial micro-expressions are rapid involuntary facial expressions which reveal suppressed affect. To the best knowledge of the authors, there is no previous work that successfully recognises spontaneous facial micro-expressions. In this paper we show how a temporal interpolation model together with the first comprehensive spontaneous micro-expression corpus enable us to accurately recognise these very short expressions. We designed an induced emotion suppression experiment to collect the new corpus using a high-speed camera. The system is the first to recognise spontaneous facial micro-expressions and achieves very promising results that compare favourably with the human micro-expression detection accuracy.
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[247] proposes the first microexpressions recognition system.
“[247] proposes the first microexpressions recognition system.”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [247]
Microexpressions detection and classification with high recognition rates are reported even at 25fps.
“Microexpressions detection and classification with high recognition rates are reported even at 25fps.”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [247]
A main trend in microexpression analysis is to use appearance representations captured locally in a dynamic way.
“For this reason, a main trend in microexpression analysis is to use appearance representations captured locally in a dynamic way [245], [246], [247].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [247]
Detection and classification of posed and more recently spontaneous microexpressions have been proposed.
“Detection and classification of posed [245], [246], [248] and more recently spontaneous microexpressions [247] have been proposed.”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [247]
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398 · see citing works
Macro- and micro-expression spotting in long videos using spatio-temporal strain
Shreve, M., Godavarthy, S., Goldgof, D., & Sarkar, S. (2011) · Face and Gesture 2011 · doi:10.1109/fg.2011.5771451 · Cited by 218 · see citing works
Towards macro- and micro-expression spotting in video using strain patterns
Shreve, M., Godavarthy, S., Manohar, V., Goldgof, D., & Sarkar, S. (2009) · 2009 Workshop on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2009.5403044 · Cited by 124 · see citing works
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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
Human and artificial intelligence collaboration for socially shared regulation in learning
Järvelä, S., Nguyen, A., & Hadwin, A. (2023) · British Journal of Educational Technology · doi:10.1111/bjet.13325 · Cited by 342 · see citing works
- Authors
- Pfister, T., Li, X., Zhao, G., & Pietikainen, M.
- Affiliations
- University of Oulu (FI)
- Venue
- 2011 International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2011-11
- Pages
- 1449-1456
- OpenAlex
- W2059068649
- Cited by
- 450
- References
- 4
- Citations by year
- 2026: 21 · 2025: 36 · 2024: 38 · 2023: 41 · 2022: 44 · 2021: 42 · 2020: 45 · 2019: 39 · 2018: 31 · 2017: 37 · 2016: 29 · 2015: 23 · 2014: 9 · 2013: 9 · 2012: 5
- FWCI
- 12.20
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- Funders
- Infotech Oulu
- 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 micro-expressions, micro-expression recognition, emotional suppression, high-speed video, affect recognition
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