Macro- and micro-expression spotting in long videos using spatio-temporal strain
Abstract
We propose a method for the automatic spotting (temporal segmentation) of facial expressions in long videos comprising of macro- and micro-expressions. The method utilizes the strain impacted on the facial skin due to the non-rigid motion caused during expressions. The strain magnitude is calculated using the central difference method over the robust and dense optical flow field observed in several regions (chin, mouth, cheek, forehead) on each subject's face. This new approach is able to successfully detect and distinguish between large expressions (macro) and rapid and localized expressions (micro). Extensive testing was completed on a dataset containing 181 macro-expressions and 124 micro-expressions. The dataset consists of 56 videos collected at USF, 6 videos from the Canal-9 political debates, and 3 low quality videos found on the internet. A spotting accuracy of 85% was achieved for macro-expressions and 74% of all micro-expressions were spotted.
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Before the publication of CASME and SMIC in 2013, methods were usually trained with posed non-public data.
“Before the publication of the CASME and the SMIC dataset in 2013, methods were usually trained with posed non-public data [245], [246], [248].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [246]
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 [246]
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 [246]
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Recognizing spontaneous micro-expression from eye region
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Automated Face Recognition: Challenges and Solutions
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A comprehensive survey on facial micro-expression: approaches and databases
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- Authors
- Shreve, M., Godavarthy, S., Goldgof, D., & Sarkar, S.
- Affiliations
- University of South Florida (US)
- Venue
- Face and Gesture 2011
- Publisher
- IEEE
- Published
- 2011-03
- Pages
- 51-56
- ISSN
- 1541-5058 (ISSN-L)
- OpenAlex
- W2056591378
- Cited by
- 218
- References
- 13
- Citations by year
- 2026: 3 · 2025: 11 · 2024: 21 · 2023: 20 · 2022: 21 · 2021: 23 · 2020: 16 · 2019: 20 · 2018: 19 · 2017: 13 · 2016: 19 · 2015: 10 · 2014: 11 · 2013: 6 · 2012: 2
- FWCI
- 5.25
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Automatic Face and Gesture Recognition (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- macro-expressions, micro-expressions, temporal segmentation, optical flow, dense optical flow, facial expression datasets
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