The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Facial expression recognition using Gabor motion energy filters
Abstract
Spatial Gabor energy filters (GE) are one of the most successful approaches to represent facial expressions in computer vision applications, including face recognition and expression analysis. It is well known that these filters approximate the response of complex cells in primary visual cortex. However these neurons are modulated by the temporal, not just spatial, properties of the visual signal. This suggests that spatio-temporal Gabor filters may provide useful representations for applications that involve video sequences. In this paper we explore Gabor motion energy filters (GME) as a biologically inspired representation for dynamic facial expressions. Experiments on the Cohn-Kanade expression dataset show that GME outperforms GE, particularly on difficult low intensity expression discrimination.
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“Mostly, all previous work treats the texture or landmark modality independently, where only the texture or landmark modality is employed for FER.”
From Multimodal learning for facial expression recognition (2015) · cites this paper as [5]
In the citing paper
“As aforementioned, the previous FER methods can be regarded as a type of two-step methods.”
From Multimodal learning for facial expression recognition (2015) · cites this paper as [5]
In the citing paper
“As stated in [5], facial dynamics is about motion among frames, rather than static patterns.”
From Multimodal learning for facial expression recognition (2015) · cites this paper as [5]
Citations
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
Robust Real-Time Face Detection
Viola, P., & Jones, M. J. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000013087.49260.fb · Cited by 14,097
Libsvm : A library for support vector machines
Ferrari, V. (2008) · Medical Entomology and Zoology · Cited by 10,123
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
Spatiotemporal energy models for the perception of motion
Adelson, E. H., & Bergen, J. R. (1985) · Journal of the Optical Society of America A · doi:10.1364/josaa.2.000284 · Cited by 3,442
Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions
Zhao, G., & Pietikainen, M. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1110 · Cited by 2,846
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
Zeng, Z., Pantic, M., Roisman, G. I., & Huang, T. S. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.52 · Cited by 2,797
Comprehensive database for facial expression analysis
Takeo Kanade, A., Cohn, J. F., & Tian, Y. (2002) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.2000.840611 · Cited by 2,644
Facial expression recognition based on Local Binary Patterns: A comprehensive study
Shan, C., Gong, S., & McOwan, P. W. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2008.08.005 · Cited by 2,300
Image representation using 2D Gabor wavelets
Lee, T. S. (1996) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.541406 · Cited by 1,632
Research works printed but not linked: 2
- movellan, 2005, Tutorial on gabor filters, Technical report MPLab Tutorials UCSD MPLab
- 10.1037/0022-3514.37.11.2049 · DOI
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Jain, A., Bhatia, K., Sharma, R., & Bhadola, S. (2019) · International Journal of Computer Science and Engineering · doi:10.14445/23488387/ijcse-v6i4p105 · Cited by 2
Performance Analysis of Similarity Coefficient Feature Vector on Facial Expression Recognition
Ane, T., & Patwary, M. F. K. (2016) · Procedia Engineering · doi:10.1016/j.proeng.2016.05.154 · Cited by 2
Video-Based Facial Expression Recognition: A Deep Learning Approach
Jacob, I. J., & Sonia, J. J. (2021) · Algorithms for intelligent systems · doi:10.1007/978-981-16-2109-3_12 · Cited by 2
Facial Emotion Recognition as Spatial Image using Gabor Filter
Luharuka, S., Meghana, R., Pallavi, K. J., & Manek, A. S. (2022) · Computer Science & Engineering An International Journal · doi:10.5121/cseij.2022.12110 · Cited by 2
Example-Based facial animation for blend shape interpolation
Acquaah, K., Agada, R., & Yan, J. (2015) · International Conference on Electrical, Computer and Communication Technologies (ICECCT) · doi:10.1109/icecct.2015.7226016 · Cited by 1
Emotion detection in sequence of images using advanced PCA with SVM
Sharma, G., & Gupta, S. (2014) · International Conference on Cloud Computing, Data Science & Engineering (Confluence) · doi:10.1109/confluence.2014.6949303 · Cited by 1
Effects of training set selection on pain recognition via facial expressions
Shier, W. A., & Yanushkevich, S. N. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2241108 · Cited by 1
Lin, C.-H., Long, F., Yao, J., Sun, M., & Su, J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-70090-8_45 · Cited by 1
Hong, 唐. T., Junling, 向. X., Haitao, 陈. C., Lü, 吕. R., & Zehao, 夏. X. (2023) · Laser & Optoelectronics Progress · doi:10.3788/lop213204 · Cited by 1
From Constricted Models to State-of-the-Art for Facial Expression Classification
Rodrigues, A. S., Lopes, J. C., & Lopes, R. P. (2025) · SN Computer Science · doi:10.1007/s42979-025-04178-9 · Cited by 1
Sobia, M. C., & Abudhahir, A. (2018) · Intelligent Automation & Soft Computing · doi:10.31209/2018.100000014 · Cited by 1
Saliency Contour Extraction Based on Multi-level Feature Channel Optimization Coding
Fang, L., & Fan, Y. (2021) · Electronics Science Technology and Application · doi:10.18686/esta.v7i4.168 · Cited by 1
Gabor Wavelets in Face Recognition and Its Applications
Ojha, M. K. (2022) · Smart innovation, systems and technologies · doi:10.1007/978-3-030-92905-3_19 · Cited by 1
Nouri, A., & Lajavardi, S. M. (2015) · DergiPark (Istanbul University) · doi:10.17776/csj.64143
An FPGA-based hardware accelerator for simulating spatiotemporal neurons
Tarawneh, G., & Read, J. C. A. (2014) · IEEE International Conference on Electronics, Circuits and Systems (ICECS) · doi:10.1109/icecs.2014.7050061
Texture-independent recognition of facial expressions in image snapshots and videos
Raducanu, B., & Dornaika, F. (2012) · Machine Vision and Applications · doi:10.1007/s00138-012-0447-z
Wavelet-threshold based Bit Intensity Measurement: On Facial Expression Recognition
Ane, T. (2016) · International Journal of Computer Applications · doi:10.5120/ijca2016907850
Avaliação da influência de emoções na tomada de decisão de sistemas computacionais.
Gracioso, A. C. N. D. R. (2016) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.3.2016.tde-28062016-085426
Real-time multi-criteria classification of facial images
Marinov, R., Chen, Z., & Reznik, Y. A. (2019) · SPIE Applications of Digital Image Processing · doi:10.1117/12.2532068
Computer-Based Facial Expression Recognition
Kumar, S. P. (2020) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2020.6196
Biologically Inspired Facial Emotion Recognition
Cruz, A. (2014) · eScholarship (California Digital Library)
Improving Facial Emotion Recognition Through Ensemble Classification Strategies
Catherine, J., & Brindha, M. (2024) · Smart innovation, systems and technologies · doi:10.1007/978-981-97-6222-4_40
Multimodal Engagement Estimation for Online Teaching through Facial and Postural Analytics
Sivasangari, V., Saranya, G., & Uma Maheswari, B. (2025) · International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS) · doi:10.1109/icuis67429.2025.11380296
Parveen, S., Das, S. K., & Tiwari, S. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-17184-9_6
Metadata
- Authors
- Wu, T., Bartlett, M. S., & Movellan, J. R.
- Affiliations
- University of California San Diego (US)
- ORCID
- Wu, Tingfan
- Venue
- 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops
- Publisher
- IEEE
- Published
- 2010-06
- Pages
- 42-47
- ISSN
- 2160-7508 (ISSN-L)
- OpenAlex
- W2107671593
- Cited by
- 126
- References
- 27
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 2 · 2023: 4 · 2022: 4 · 2021: 6 · 2020: 6 · 2019: 12 · 2018: 9 · 2017: 12 · 2016: 11 · 2015: 21 · 2014: 19 · 2013: 7 · 2012: 9
- FWCI
- 4.66
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Keywords
- facial expression recognition, dynamic facial expressions, video sequences
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