The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
A Main Directional Mean Optical Flow Feature for Spontaneous Micro-Expression Recognition
Abstract
Micro-expressions are brief facial movements characterized by short duration, involuntariness and low intensity. Recognition of spontaneous facial micro-expressions is a great challenge. In this paper, we propose a simple yet effective Main Directional Mean Optical-flow (MDMO) feature for micro-expression recognition. We apply a robust optical flow method on micro-expression video clips and partition the facial area into regions of interest (ROIs) based partially on action units. The MDMO is a ROI-based, normalized statistic feature that considers both local statistic motion information and its spatial location. One of the significant characteristics of MDMO is that its feature dimension is small. The length of a MDMO feature vector is 36 × 2 = 72, where 36 is the number of ROIs. Furthermore, to reduce the influence of noise due to head movements, we propose an optical-flow-driven method to align all frames of a micro-expression video clip. Finally, a SVM classifier with the proposed MDMO feature is adopted for micro-expression recognition. Experimental results on three spontaneous micro-expression databases, namely SMIC, CASME and CASME II, show that the MDMO can achieve better performance than two state-of-the-art baseline features, i.e., LBP-TOP and HOOF.
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Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
Cootes, T. F., Edwards, G. J., & Taylor, C. J. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.927467 · Cited by 5,409
A Database and Evaluation Methodology for Optical Flow
Baker, S., Scharstein, D., Lewis, J. P., Roth, S., Black, M. J., & Szeliski, R. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-010-0390-2 · Cited by 2,205 · cited 1×
“Optical flow fields in (b), (d) and (f) are visualized using the color coding scheme in [31].”
MethodAutomatic analysis of facial expressions: the state of the art
Pantie, M., & Rothkrantz, L. J. M. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.895976 · Cited by 1,778
CASME II: An Improved Spontaneous Micro-Expression Database and the Baseline Evaluation
Yan, W.-J., Li, X., Wang, S.-J., Zhao, G., Liu, Y.-J., Chen, Y.-H., & Fu, X. (2014) · PLoS ONE · doi:10.1371/journal.pone.0086041 · Cited by 993
Self-citationMicromomentary facial expressions as indicators of ego mechanisms in psychotherapy
Haggard, E. A., & Isaacs, K. S. (1966) · Methods of Research in Psychotherapy · doi:10.1007/978-1-4684-6045-2_14 · Cited by 343 · cited 1×
“Micro-expressions, once named micro-momentary facial expressions that might be unknown to or uncontrollable for humans, were first discovered in 1966 [3].”
BackgroundFacial micro-expressions recognition using high speed camera and 3D-gradient descriptor
Polikovsky, S., Kameda, Y., & Ohta, Y. (2009) · 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP 2009) · doi:10.1049/ic.2009.0244 · Cited by 312
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
Cited by
OFF-ApexNet on micro-expression recognition system
Gan, Y. S., Liong, S.-T., Yau, W.-C., Huang, Y.-C., & Tan, L.-K. (2019) · Signal Processing: Image Communication · doi:10.1016/j.image.2019.02.005 · Cited by 303
Shallow Triple Stream Three-dimensional CNN (STSTNet) for Micro-expression Recognition
Liong, S.-T., Gan, Y. S., See, J., Khor, H.-Q., & Huang, Y.-C. (2019) · 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019) · doi:10.1109/fg.2019.8756567 · Cited by 253
Automated emotion recognition: Current trends and future perspectives
Maithri, M., Raghavendra, U., Gudigar, A., Samanth, J., Barua, P. D., Murugappan, M., Chakole, Y., & Acharya, U. R. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106646 · Cited by 172
Metadata
- Authors
- Liu, Y.-J., Zhang, J.-K., Yan, W.-J., Wang, S.-J., Zhao, G., & Fu, X.
- Venue
- IEEE Transactions on Affective Computing
- Published
- 2016-10-01
- Volume
- 7
- Issue
- 4
- Pages
- 299-310
- ISSN
- 1949-3045 (ISSN-L)
- 2371-9850
- OpenAlex
- W2526853616
- Cited by
- 520
- References
- 8
- FWCI
- 29.22
- Type
- journal article
- Language
- en
- Open access
- closed
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- Liu, Y.-J., Zhang, J.-K., Yan, W.-J., Wang, S.-J., Zhao, G., & Fu, X. (2016). A Main Directional Mean Optical Flow Feature for Spontaneous Micro-Expression Recognition. IEEE Transactions on Affective Computing. https://doi.org/10.1109/taffc.2015.2485205
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