The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Automatic facial expression recognition on a single 3D face by exploring shape deformation
Abstract
Facial expression recognition has many applications in multimedia processing and the development of 3D data acquisition techniques makes it possible to identify expressions using 3D shape information. In this paper, we propose an automatic facial expression recognition approach based on a single 3D face. The shape of an expressional 3D face is approximated as the sum of two parts, a basic facial shape component (BFSC) and an expressional shape component (ESC). The BFSC represents the basic face structure and neutral-style shape and the ESC contains shape changes caused by facial expressions. To separate the BFSC and ESC, our method firstly builds a reference face for each input 3D non-neutral face by a learning method, which well represents the basic facial shape. Then, based on the BFSC and the original expressional face, a facial expression descriptor is designed. The surface depth changes are considered in the descriptor. Finally, the descriptor is input into an SVM to recognize the expression. Unlike previous methods which recognize a facial expression with the help of manually labeled key points and/or a neutral face, our method works on a single 3D face without any manual assistance. Extensive experiments are carried out on the BU-3DFE database and comparisons with existing methods are conducted. The experimental results show the effectiveness of our method.
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A Systematic Assessment on 3D-Based Deep Learning Models and Challenges in FER
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Style-exprGAN: Diverse Smile Style Image Generation Via Attention-Guided Adversarial Networks
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Metadata
- Authors
- Gong, B., Wang, Y., Liu, J., & Tang, X.
- Affiliations
- Chinese University of Hong Kong (HK)
- Venue
- Proceedings of the 17th ACM international conference on Multimedia
- Publisher
- ACM
- Published
- 2009-10-19
- Pages
- 569-572
- OpenAlex
- W1978435065
- Cited by
- 116
- References
- 18
- Citations by year
- 2025: 1 · 2024: 3 · 2023: 4 · 2022: 5 · 2021: 16 · 2020: 11 · 2019: 12 · 2018: 9 · 2017: 4 · 2016: 9 · 2015: 7 · 2014: 10 · 2013: 7 · 2012: 6
- FWCI
- 4.90
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Conference on Multimedia (ACM MM) (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
- facial expression recognition, 3D face shape, shape deformation, BU-3DFE
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