Clustering of video-patches on Grassmannian manifold for facial expression recognition from 3D videos
Abstract
This paper presents a fully automatic system which exploits the dynamics of 3D videos and is capable of recognizing six basic facial expressions. Local video-patches of variable lengths are extracted from different locations of the training videos and represented as points on the Grass-mannian manifold. An efficient spectral clustering based algorithm is used to separately cluster points for each of the six expression classes. The resulting cluster centers are matched with the points of a test video and a voting based strategy is used to decide about the expression class of the test video. The proposed system is tested on the largest publicly available 3D video database, BU4DFE. The experimental results show that the system achieves a very high classification accuracy and outperforms the current state of the art algorithms for facial expression recognition from 3D videos.
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Many methods focus on recognizing a limited set of primary emotions (usually 6), mainly due to a lack of more diverse datasets.
“Many methods focus on recognizing a limited set of primary emotions (usually 6) [115], [116], [123], [130], [137], [145], [146]. This is mainly due to a lack of more diverse datasets.”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [123]
Cites
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398 · see citing works
A tutorial on spectral clustering
von Luxburg, U. (2007) · Statistics and Computing · doi:10.1007/s11222-007-9033-z · Cited by 10,519 · see citing works
The Geometry of Algorithms with Orthogonality Constraints
Edelman, A. S., Arias, T. A., & Smith, S. T. (1998) · SIAM Journal on Matrix Analysis and Applications · doi:10.1137/s0895479895290954 · Cited by 2,773 · see citing works
Riemannian center of mass and mollifier smoothing
Karcher, H. (1977) · Communications on Pure and Applied Mathematics · doi:10.1002/cpa.3160300502 · Cited by 995 · see citing works
Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations
Kim, T., Kittler, J., & Cipolla, R. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1037 · Cited by 602 · see citing works
An Efficient Multimodal 2D-3D Hybrid Approach to Automatic Face Recognition
Mian, A. S., Bennamoun, M., & Owens, R. A. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1105 · Cited by 469 · see citing works
Self-citationStatistical Computations on Grassmann and Stiefel Manifolds for Image and Video-Based Recognition
Turaga, P. K., Veeraraghavan, A., Srivastava, A., et al. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.52 · Cited by 385 · 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
Nonlinear Mean Shift over Riemannian Manifolds
Subbarao, R., & Meer, P. (2009) · International Journal of Computer Vision · doi:10.1007/s11263-008-0195-8 · Cited by 154 · see citing works
Facial Expression Recognition Based on 3D Dynamic Range Model Sequences
Sun, Y., & Yin, L. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88688-4_5 · Cited by 120 · see citing works
Recognition of 3D facial expression dynamics
Sandbach, G., Zafeiriou, S. P., Pantić, M., & Rueckert, D. (2012) · Image and Vision Computing · doi:10.1016/j.imavis.2012.01.006 · Cited by 115 · see citing works
Intrinsic mean shift for clustering on Stiefel and Grassmann manifolds
Çetingül, H. E., & Vidal, R. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2009.5206806 · Cited by 108 · see citing works
3D facial expression recognition: A perspective on promises and challenges
Fang, T., Zhao, X., Ocegueda, O., Shah, S. K., & Kakadiaris, I. A. (2011) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2011.5771466 · Cited by 86 · see citing works
A dynamic approach to the recognition of 3D facial expressions and their temporal models
Sandbach, G., Zafeiriou, S. P., Pantić, M., & Rueckert, D. (2011) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2011.5771434 · Cited by 79 · 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
A training-free nose tip detection method from face range images
Peng, X., Bennamoun, M., & Mian, A. S. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.09.015 · Cited by 48 · see citing works
Self-citationA Multiple View 3D Registration Algorithm with Statistical Error Modeling
Williams, J. A., & Bennamoun, M. (2000) · Cited by 33 · see citing works
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Corneanu, 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
- Authors
- Hayat, M., Bennamoun, M., & El-Sallam, A. A.
- Affiliations
- The University of Western Australia (AU)
- Venue
- 2013 IEEE Workshop on Applications of Computer Vision (WACV)
- Publisher
- IEEE
- Published
- 2013-01
- Volume
- 83
- Pages
- 83-88
- ISSN
- 1550-5790 (ISSN-L)
- OpenAlex
- W2117852230
- Cited by
- 14
- References
- 17
- Citations by year
- 2024: 1 · 2023: 1 · 2022: 1 · 2019: 3 · 2017: 1 · 2016: 2 · 2014: 4 · 2013: 1
- FWCI
- 1.49
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings (publishedVersion)
- UWA Profiles and Research Repository (University of Western Australia) (submittedVersion)
- Funders
- Society of Interventional Radiology 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
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- facial expression recognition, 3D video, Grassmannians, spectral clustering, voting strategy
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