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Towards macro- and micro-expression spotting in video using strain patterns
Abstract
This paper presents a novel method for automatic spotting (temporal segmentation) of facial expressions in long videos comprising of continuous and changing 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 of each subjects face. Testing has been done on 2 datasets (which includes 100 macro-expressions) and promising results have been obtained. The method is robust to several common drawbacks found in automatic facial expression segmentation including moderate in-plane and out-of-plane motion. Additionally, the method has also been modified to work with videos containing micro-expressions. Micro-expressions are detected utilizing their smaller spatial and temporal extent. A subject's face is divided in to sub-regions (mouth, cheeks, forehead, and eyes) and facial strain is calculated for each of these regions. Strain patterns in individual regions are used to identify subtle changes which facilitate the detection of micro-expressions.
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In the citing paper
“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 [245]
In the citing paper
“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 [245]
In the citing paper
“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 [245]
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Bhaskar, H., Torre, D. L., & Al-Mualla, M. E. (2016) · Springer eBooks · doi:10.1007/978-3-319-25958-1_5 · Cited by 1
Xiaochen, D., Zhi-gang, Z., & Qiang, L. (2018) · International Conference on Communication and Information Processing (ICCIP) · doi:10.1145/3290420.3290421 · Cited by 1
Metadata
- Authors
- Shreve, M., Godavarthy, S., Manohar, V., Goldgof, D., & Sarkar, S.
- Affiliations
- University of South Florida (US)
- Venue
- 2009 Workshop on Applications of Computer Vision (WACV)
- Publisher
- IEEE
- Published
- 2009-12
- Pages
- 1-6
- ISSN
- 2472-6737 (ISSN-L)
- OpenAlex
- W2113977317
- Cited by
- 124
- References
- 18
- Citations by year
- 2026: 1 · 2025: 10 · 2024: 9 · 2023: 7 · 2022: 6 · 2021: 14 · 2020: 7 · 2019: 14 · 2018: 11 · 2017: 12 · 2016: 10 · 2015: 7 · 2014: 6 · 2013: 3 · 2012: 1
- FWCI
- 1.90
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Winter Conference on Applications of Computer Vision (publishedVersion)
- Funders
- U.S. Forest Service
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Artificial Intelligence › 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
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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