The DOI matches its Crossref record; 17 of the 18 references it cites were found in a registry or an index.
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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Each is checked against this paper’s own text: does it say what the citing paper says it does?
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]
Citations
Cites
Performance of optical flow techniques
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A 3D Facial Expression Database For Facial Behavior Research
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Ekman, P. (1992) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.1992.0008 · Cited by 432
Tissue elasticity reconstruction using linear perturbation method
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Facial expression recognition using a dynamic model and motion energy
Essa, I. A., & Pentland, A. (2002) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466916 · Cited by 253
Facial expression decomposition
Wang, H., & Ahuja (2003) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2003.1238452 · Cited by 199
Manifold Based Analysis of Facial Expression
Hu, C., Chang, Y. H., Feris, R. S., & Turk, M. (2005) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvpr.2004.390 · Cited by 74
Temporal Segmentation of Facial Behavior
la Torre, F. D., Campoy, J., Ambadar, Z., & Cohn, J. F. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4408961 · Cited by 66
Silent talker: a new computer-based system for the analysis of facial cues to deception
Rothwell, J., Bandar, Z. A., O’Shea, J. D., & McLean, D. (2006) · Applied Cognitive Psychology · doi:10.1002/acp.1204 · Cited by 51
Facial Expression Analysis Using Nonlinear Decomposable Generative Models
Lee, C.-S., & Elgammal, A. (2005) · Lecture notes in computer science · doi:10.1007/11564386_3 · Cited by 45
A probabilistic framework for rigid and non-rigid appearance based tracking and recognition
la Torre, F. D., Yacoob, Y., & Davis, L. S. (2002) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.2000.840679 · Cited by 38
Facial Strain Pattern as a Soft Forensic Evidence
Manohar, V., Goldgof, D. B., Sarkar, S., & Zhang, Y. (2007) · Proceedings · doi:10.1109/wacv.2007.23 · Cited by 15
Self-citationFinite element modeling of facial deformation in videos for computing strain pattern
Manohar, V., Shreve, M., Goldgof, D. B., & Sarkar, S. (2008) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2008.4761314 · Cited by 7
Self-citation
Research works printed but not linked: 1
- 0, Optical Flow source
Cited by
Sorted by citations
A Spontaneous Micro-expression Database: Inducement, collection and baseline
Li, X., Pfister, T., Huang, X., Zhao, G., & Pietikäinen, M. (2013) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2013.6553717 · Cited by 703
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
Recognising spontaneous facial micro-expressions
Pfister, T., Li, X., Zhao, G., & Pietikainen, M. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126401 · Cited by 450
Less is more: Micro-expression recognition from video using apex frame
Liong, S., See, J., Wong, K., & Phan, R. C. ‐. (2017) · Signal Processing Image Communication · doi:10.1016/j.image.2017.11.006 · Cited by 414
CASME database: A dataset of spontaneous micro-expressions collected from neutralized faces
Yan, W., Wu, Q., Liu, Y., Wang, S., & Fu, X. (2013) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2013.6553799 · Cited by 363
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
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