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
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
Improved boosting algorithms using confidence-rated predictions
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
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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
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Sorted by citations
Items 101–139 of 139
Human emotion recognition using a deformable 3D facial expression model
Tie, Y., & Guan, L. (2012) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2012.6271426 · Cited by 6
A Comparison of Methods of Facial Expression Recognition
Chen, Y., Wu, H., Wang, T., & Wang, Y. (2018) · WRC Symposium on Advanced Robotics and Automation (WRC SARA) · doi:10.1109/wrc-sara.2018.8584202 · Cited by 6
Identifying universal facial emotion markers for automatic 3D facial expression recognition
Azazi, A., Lutfi, S. L., & Venkat, I. (2014) · International Conference on Computer and Information Sciences · doi:10.1109/iccoins.2014.6868369 · Cited by 5
Discriminative dictionary learning sparse coding for person re-identification
Sheng, H., Zhang, B., Huang, Y., Zheng, Y., & Xiong, Z. (2016) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2016.7535564 · Cited by 4
Recognizing Surgically Altered Face Images and 3D Facial Expression Recognition
Sruthy, B. S., & Jayasree, M. (2016) · Procedia Technology · doi:10.1016/j.protcy.2016.05.122 · Cited by 4
A Trusted Affective Model Approach to Proactive Health Monitoring System
Xu, C., & Feng, Z. (2008) · International Conference on Future BioMedical Information Engineering · doi:10.1109/fbie.2008.52 · Cited by 4
Perception driven 3D facial expression analysis based on reverse correlation and normal component
Zhang, X., Zhang, Z., Yin, L., Hipp, D., & Gerhardstein, P. C. (2015) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2015.7344633 · Cited by 4
On Decomposing an Unseen 3D Face into Neutral Face and Expression Deformations
Al-Osaimi, F. R., Bennamoun, M., & Mian, A. S. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-01793-3_3 · Cited by 3
An approach to facial expression analysis with multi-model interactions
Xu, C., Hu, Q., Xu, G., & Feng, Z. (2013) · International Journal of Computer Mathematics · doi:10.1080/00207160.2013.802309 · Cited by 3
3D facial expression recognition based on variation faces
Li, X., Ruan, Q., An, G., & Ruan, C. (2012) · International Conference on Signal Processing · doi:10.1109/icosp.2012.6491602 · Cited by 2
A Novel 3D Facial Expression Recognition Approach Based on Tensor Distance
Li, Q., & Ruan, Q. (2018) · IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) · doi:10.1109/imcec.2018.8469297 · Cited by 2
3D Face Analysis: Advances and Perspectives
Huang, D., Sun, J., Yang, X., Weng, D., & Wang, Y. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-12484-1_1 · Cited by 2
Facial expression recognition based on spatio-temporal interest points for depth sequences
Duh, D. J., Huang, J.-R., Chen, S., Su, S., Zhang, H., & Li, S. (2016) · The Imaging Science Journal · doi:10.1080/13682199.2016.1227515 · Cited by 2
Shape-based facial expression classification using Angular Radial Transform
Turan, M., & Ekenel, H. K. (2013) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2013.6531564 · Cited by 2
Recognizing facial expressions from 3D video: Current results and future prospects
Malassiotis, S., & Tsalakanidou, F. (2011) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2011.5771465 · Cited by 2
Probabilistic Topic Models for Human Emotion Analysis
Lade, P. (2015) · Research Archive of Indian Institute of Technology Hyderabad (Indian Institute of Technology Hyderabad) · Cited by 2
Intelligent recognition of emotional expressions in 3D face images
Khashman, A., & Conkbayir, F. O. (2013) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2013.6531222 · Cited by 2
Real Time 3D Facial Emotion Classification using a Digital Signal PIC Microcontroller
Fnaiech, A., Bouzaiane, S., Sayadi, M., Louis, N., & Gorce, P. (2018) · IEEE International Conference on Image Processing, Applications and Systems (IPAS) · doi:10.1109/ipas.2018.8708856 · Cited by 2
Classificação de Gamers por Padrões em suas Expressões Faciais
Jofilsan, N. C., de Lima, S. M. L., Alencar, R. C., Silva, S. H. M. T., de Albuquerque, J. V. O., Filho, O. H. S., Maciel, A. M. A., & dos Santos, W. P. (2021) · Simpósio Brasileiro de Jogos e Entretenimento Digital (SBGames) · doi:10.5753/sbgames_estendido.2021.21516 · Cited by 1
Edge detection for facial expression recognition.
García-Ramírez, J., Olmos-Pineda, I., Olvera-López, J. A., & Ortiz, M. M. (2016) · Cited by 1
Training on Statistical Feature Models of Action Units for 3D Facial Expression Recognition
Dong, Z., Jia, X., Gao, W., & Wang, K. (2018) · International Conference on Information Science and Cloud Computing Companion (ISCC-C) · doi:10.22323/1.300.0021 · Cited by 1
Facial Expression Recognition Based on Spatial Transformer Siamese Networks
Luo, S., Zhang, X., Guo, Y., & Bai, S. (2018) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/compcomm.2018.8780988 · Cited by 1
Wavelet-based Recognition of Facial Expressions and Faces
Poursaberi, A. (2014) · Libraries and Cultural Resources (University of Calgary) · doi:10.11575/prism/28676
Automatic Registration ofVertex Correspondences for3DFacial Expression Analysis
Rosato, M., & Chen, X. (2008)
Analyse locale de la forme 3D pour la reconnaissance d'expressions faciales
Maalej, A., Amor, B. B., & Daoudi, M. (2011) · HAL
Subject Independent Facial Expression Recognition from 3D Face Models using Deformation Modeling
Srivastava, R., Yan, S., Sim, T., & Ranganath, S. (2011) · IGI Global eBooks · doi:10.4018/978-1-61350-326-3.ch030
Thresholding Approach based on GPU for Facial Expression Recognition
García-Ramírez, J., Olvera-López, J. A., Olmos-Pineda, I., Becerra, G. F., & Rico, A. A. (2016) · Research in Computing Science · doi:10.13053/rcs-112-1-4
Enhancing 3D Facial Expression Recognition by Exaggerating Geometry Characteristics
Li, W., Wang, Y., Li, H., & Huang, D. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-69923-3_21
Landmark-based Multi-Points Warping Approach to 3D Facial Expression Recognition in Human
Agbolade, O., Nazri, A., Yaakob, R., Ghani, A., & Cheah, Y. K. (2019) · International Conference on Artificial Intelligence and Data Sciences (AiDAS) · doi:10.1109/aidas47888.2019.8970972
Entropy Based Feature Selection for 3D Facial Expression Recognition
Yurtkan, K. (2014)
Fishman, J. Z. (2020) · TSpace (University of Toronto)
An Effective Adoption of Disparity to Enhance Recognizing Three-Dimension Facial Expression
Shin, K., & Chung, K.-D. (2013) · Lecture notes in electrical engineering · doi:10.1007/978-3-642-41671-2_94
A Deep Learning Based Approach To Recognize Real Time Emotions
Vatsa, A., & Gupta, S. (2022) · International Conference on Contemporary Computing and Informatics (IC3I) · doi:10.1109/ic3i56241.2022.10072931
Human Computer Interaction using CNN and Fuzzy Inference System (FIS)
(2024) · Nanotechnology Perceptions · doi:10.62441/nano-ntp.v20is2.20
Jia, G., & Gao, X. (2024) · Journal of Organizational and End User Computing · doi:10.4018/joeuc.347217
Measuring Facial Expressions in Italian Sign Language Using ARKit: Repeatability Study
Spagna, M., Taborri, J., & Rossi, S. (2026) · IEEE International Symposium on Medical Measurements and Applications (MeMeA) · doi:10.1109/memea69746.2026.11537329
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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