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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Multi-Feature Tensor Neighborhood Preserving Embedding for 3D Facial Expression Recognition
Jiang, Y., & Ruan, Q. (2021) · IEEE Access · doi:10.1109/access.2021.3101042 · Cited by 2 · see citing works
Kaiser, M., Arsić, D., Sural, S., & Rigoll, G. (2010) · International Conference on Computer Vision Theory and Applications · doi:10.5220/0002819302810286 · Cited by 2 · see citing works
Robust tracking of facial feature points with 3D Active Shape Models
Kaiser, M. S., Arsić, D., Sural, S., & Rigoll, G. (2010) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2010.5654057 · Cited by 2 · see citing works
Integration of 2D Textural and 3D Geometric Features for Robust Facial Expression Recognition
Adjailia, F., Ramdani, M., Rasamoelina, A. D., & Sinčák, P. (2021) · Computing and Informatics · doi:10.31577/cai_2021_5_988 · Cited by 2 · see citing works
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Emotions, Attitudes, and Challenges in the Perception of Artificial Intelligence in Social Research
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Retracted: Expressions Recognition Based on Human Face
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Retracted3D-Based Facial Emotion Recognition using Depthwise Separable Convolution
Abubakar, H. S., Hossin, M. A., Yussif, S. B., Fargalla, M. A. M., Said, R. R., Umar, Y. J., Chima, U. C., & Kuupole, E. E. (2022) · International Conference on Machine Learning and Computing (ICMLC) · doi:10.1145/3529836.3529855 · Cited by 1 · see citing works
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Recuperação de imagens cardiacas tridimensionais por conteúdo
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A novel approach to enhance automatic 3D facial expression recognition
Li, X., Ruan, Q., Jin, Y., & An, G. (2014) · IEEE International Conference on Multimedia and Expo workshops · doi:10.1109/icmew.2014.6890675
- 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
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- facial expression recognition, 3D face shape, shape deformation, BU-3DFE
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.
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