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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In the citing paper
“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
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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
Cited by
Sorted by citations
Automatic Analysis of Facial Affect: A Survey of Registration, Representation, and Recognition
Sarıyanidi, E., Güneş, H., & Cavallaro, A. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2366127 · Cited by 702
Selective Transfer Machine for Personalized Facial Expression Analysis
Chu, W.-S., la Torre, F. D., & Cohn, J. F. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2547397 · Cited by 190
Facial Emotion Recognition: A Survey and Real-World User Experiences in Mixed Reality
Mehta, D., Siddiqui, M. F. H., & Javaid, A. Y. (2018) · Sensors · doi:10.3390/s18020416 · Cited by 197
Learning Affective Video Features for Facial Expression Recognition via Hybrid Deep Learning
Zhang, S., Pan, X., Cui, Y., Zhao, X., & Liu, L. (2019) · IEEE Access · doi:10.1109/access.2019.2901521 · Cited by 154
A comprehensive survey on automatic facial action unit analysis
Zhi, R., Liu, M., & Zhang, D. (2019) · The Visual Computer · doi:10.1007/s00371-019-01707-5 · Cited by 137
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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