The DOI matches its Crossref record; 11 of the 15 references it cites were found in a registry or an index.
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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“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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Cites
A decision-theoretic generalization of on-line learning and an application to boosting
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Schapire, R. E., & Singer, Y. (1998) · Conference on Learning Theory · doi:10.1145/279943.279960 · Cited by 2,549
Improved Boosting Algorithms Using Confidence-rated Predictions
Schapire, R. E., & Singer, Y. (1999) · Machine Learning · doi:10.1023/a:1007614523901 · Cited by 1,964
A 3D Facial Expression Database For Facial Behavior Research
Yin, L., Wei, X., Sun, Y., Wang, J., & Rosato, M. J. (2006) · 7th International Conference on Automatic Face and Gesture Recognition (FGR06) · doi:10.1109/fgr.2006.6 · Cited by 1,171
Three-Dimensional Face Recognition
Bronstein, A. M., Bronstein, M. M., & Kimmel, R. (2005) · International Journal of Computer Vision · doi:10.1007/s11263-005-1085-y · Cited by 430
MPEG-4 Facial Animation: The Standard,Implementation and Applications
Pakštas, A., Forchheimer, R., & Pandzic, I. S. (2002) · John Wiley & Sons, Inc. eBooks · Cited by 346
3D Facial Expression Recognition Based on Primitive Surface Feature Distribution
Wang, J., Yin, L., Wei, X., & Sun, Y. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.14 · Cited by 307
Facial Expression Recognition Using a Neural Network
Lisetti, C., & Rumelhart, D. E. (1998) · Cited by 184
Facial Expression Recognition Using 3D Facial Feature Distances
Soyel, H., & Demirel, H. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74260-9_74 · Cited by 146
Passalis, G., Kakadiaris, I. A., Theoharis, T., Toderici, G. D., & Murtuza, N. (2006) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvpr.2005.573 · Cited by 106
(2013) · Springer eBooks · doi:10.1007/978-1-4419-9863-7_100750 · Cited by 103
Flagged first, then by citations
4 of the 15 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- ekman, 0, Final report to NSF of the planning workshop on facial expression understanding
- ekman, 1978, Facial Action Coding System A Technique for the Measurement of Facial Movement
Cited by
Sorted by citations
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
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
Multimodal 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
A Set of Selected SIFT Features for 3D Facial Expression Recognition
Berretti, S., Bimbo, A. D., Pala, P., Amor, B. B., & Daoudi, M. (2010) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2010.1002 · Cited by 156
Multi-View Facial Expression Recognition Based on Group Sparse Reduced-Rank Regression
Zheng, W. (2014) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2014.2304712 · Cited by 145
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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