3D Facial Expression Recognition Based on Primitive Surface Feature Distribution
Abstract
The creation of facial range models by 3D imaging systems has led to extensive work on 3D face recognition [19]. However, little work has been done to study the usefulness of such data for recognizing and understanding facial expressions. Psychological research shows that the shape of a human face, a highly mobile facial surface, is critical to facial expression perception. In this paper, we investigate the importance and usefulness of 3D facial geometric shapes to represent and recognize facial expressions using 3D facial expression range data. We propose a novel approach to extract primitive 3D facial expression features, and then apply the feature distribution to classify the prototypic facial expressions. In order to validate our proposed approach, we have conducted experiments for person-independent facial expression recognition using our newly created 3D facial expression database. We also demonstrate the advantages of our 3D geometric based approach over 2D texture based approaches in terms of various head poses.
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Most 3D methods target primary expressions but studies about AUs were published as well.
“Most of them target primary expressions [131] but studies about AUs were published as well [122], [218].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [131]
Most 3D methods use geometric representations of the 3D faces.
“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 [131]
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Automatic analysis of facial expressions: the state of the art
Pantie, M., & Rothkrantz, L. J. M. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.895976 · Cited by 1,778 · cited 3× · see citing works
“There is a long history of interest in the problem of recognizing human emotion from facial expressions, as well as extensive studies on face perception over the last three decades [8, 17].”
BackgroundRecognizing action units for facial expression analysis
Tian, Y.-I., Kanade, T., & Cohn, J. F. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.908962 · Cited by 1,745 · see citing works
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BP4D-Spontaneous: a high-resolution spontaneous 3D dynamic facial expression database
Zhang, X., Yin, L., Cohn, J. F., Canavan, S., Reale, M., Horowitz, A., Liu, P., & Girard, J. M. (2014) · Image and Vision Computing · doi:10.1016/j.imavis.2014.06.002 · Cited by 745 · see citing works
Self-citationCorneanu, C. A., Simon, M. O., Cohn, J. F., & Guerrero, S. E. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2515606 · Cited by 527 · see citing works
Static and dynamic 3D facial expression recognition: A comprehensive survey
Sandbach, G., Zafeiriou, S., Pantic, M., & Yin, L. (2012) · Image and Vision Computing · doi:10.1016/j.imavis.2012.06.005 · Cited by 357 · see citing works
Self-citationMultimodal 2D+3D Facial Expression Recognition With Deep Fusion Convolutional Neural Network
Li, H., Sun, J., Xu, Z., & Chen, L. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2713408 · Cited by 211 · see citing works
A survey of affect recognition methods: audio, visual and spontaneous expressions
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3D facial expression recognition using SIFT descriptors of automatically detected keypoints
Berretti, S., Ben Amor, B., Daoudi, M., & del Bimbo, A. (2011) · The Visual Computer · doi:10.1007/s00371-011-0611-x · Cited by 138 · see citing works
3D facial expression recognition based on automatically selected features
Tang, H., & Huang, T. S. (2008) · 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2008.4563052 · Cited by 140 · see citing works
Real-time 2D+3D facial action and expression recognition
Tsalakanidou, F., & Malassiotis, S. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2009.12.009 · Cited by 125 · see citing works
Automatic facial expression recognition on a single 3D face by exploring shape deformation
Gong, B., Wang, Y., Liu, J., & Tang, X. (2009) · Proceedings of the 17th ACM international conference on Multimedia · doi:10.1145/1631272.1631358 · Cited by 116 · see citing works
Expression recognition from 3D dynamic faces using robust spatio-temporal shape features
Le, V., Tang, H., & Huang, T. S. (2011) · Face and Gesture 2011 · doi:10.1109/fg.2011.5771435 · Cited by 67 · see citing works
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- Authors
- Wang, J., Yin, L., Wei, X., & Sun, Y.
- Affiliations
- Binghamton University (US)
- Venue
- 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06)
- Publisher
- IEEE
- Published
- 2006-07-10
- Volume
- 2
- Pages
- 1399-1406
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2136063698
- Cited by
- 307
- References
- 2
- Citations by year
- 2026: 1 · 2024: 1 · 2023: 1 · 2022: 3 · 2021: 12 · 2020: 18 · 2019: 14 · 2018: 19 · 2017: 16 · 2016: 16 · 2015: 27 · 2014: 27 · 2013: 24 · 2012: 27
- FWCI
- 15.01
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Funders
- National Science Foundation (0414029, 0541044)
- 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
- 3D facial expression recognition, feature distribution, user-independent recognition, head pose robustness, facial expression perception
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