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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]
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“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]
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“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]
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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
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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