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3D facial expression recognition based on automatically selected features
Abstract
In this paper, the problem of person-independent facial expression recognition from 3D facial shapes is investigated. We propose a novel automatic feature selection method based on maximizing the average relative entropy of marginalized class-conditional feature distributions and apply it to a complete pool of candidate features composed of normalized Euclidean distances between 83 facial feature points in the 3D space. Using a regularized multi-class AdaBoost classification algorithm, we achieve a 95.1% average recognition rate for six universal facial expressions on the publicly available 3D facial expression database BU-3DFE [1], with a highest average recognition rate of 99.2% for the recognition of surprise. We compare these results with the results based on a set of manually devised features and demonstrate that the auto features yield better results than the manual features. Our results outperform the results presented in the previous work [2] and [3], namely average recognition rates of 83.6% and 91.3% on the same database, respectively.
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In the citing paper
“Most use geometric representations of the 3D faces, like principal directions of surface curvatures to obtain robustness to head rotations [131], and normalized Euclidean distances between fiducial points in the 3D space [118].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [118]
In the citing paper
“In the first part of the decade static representations were the primary choice in both RGB [99], [139], 3D [118], [120], [125], [131], [133], [219] and thermal [111].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [118]
In the citing paper
“Starting with the publication of the BU-3DFE dataset which provides four different intensity levels for every expression, advancements in recognizing primary expressions from 3D samples were made [118], [120], [124], [125], [131], [133], [217].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [118]
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Metadata
- Authors
- Tang, H., & Huang, T. S.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- ORCID
- Huang, Thomas S.
- Venue
- 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
- Publisher
- IEEE
- Published
- 2008-06
- Pages
- 1-8
- ISSN
- 2160-7508 (ISSN-L)
- OpenAlex
- W2166034132
- Cited by
- 140
- References
- 15
- Citations by year
- 2026: 1 · 2024: 2 · 2023: 1 · 2022: 2 · 2021: 3 · 2020: 3 · 2019: 6 · 2018: 10 · 2017: 7 · 2016: 10 · 2015: 11 · 2014: 13 · 2013: 9 · 2012: 18
- FWCI
- 11.75
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Keywords
- 3D facial expression recognition, user-independent recognition, automatic feature selection, Kullback-Leibler divergence, facial feature points, BU-3DFE, surprise expression
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