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 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 [118]
In the first part of the decade static representations were the primary choice in RGB, 3D and thermal.
“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]
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.
“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 · see citing works
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 · see citing works
Improved Boosting Algorithms Using Confidence-rated Predictions
Schapire, R. E., & Singer, Y. (1999) · Machine Learning · doi:10.1023/a:1007614523901 · Cited by 1,964 · see citing works
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 · see citing works
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 425 · see citing works
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 · see citing works
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 · see citing works
Facial Expression Recognition Using a Neural Network
Lisetti, C., & Rumelhart, D. E. (1998) · Cited by 184 · see citing works
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 · see citing works
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 · see citing works
(2013) · Springer eBooks · doi:10.1007/978-1-4419-9863-7_100750 · Cited by 103 · see citing works
Flagged first, then by citations
4 of the 15 references couldn’t be matched to a known paper.
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 · 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
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 · see citing works
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 · see citing works
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 · see citing works
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
Automatic Face Segmentation and Facial Landmark Detection in Range Images
Segundo, M. P., da Silva, L. T., Bellon, O. R. P., & Queirolo, C. C. (2010) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2009.2038233 · Cited by 137 · see citing works
Regression-based intensity estimation of facial action units
Savran, A., Sankur, B., & Taha Bilge, M. (2012) · Image and Vision Computing · doi:10.1016/j.imavis.2011.11.008 · Cited by 129 · 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
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
Comparative evaluation of 3D vs. 2D modality for automatic detection of facial action units
Savran, A., Sankur, B., & Taha Bilge, M. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2011.07.022 · Cited by 111 · see citing works
An efficient multimodal 2D + 3D feature-based approach to automatic facial expression recognition
Li, H., Ding, H., Huang, D., Wang, Y., Zhao, X., Morvan, J., & Chen, L. (2015) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2015.07.005 · Cited by 99 · see citing works
Muscular Movement Model-Based Automatic 3D/4D Facial Expression Recognition
Zhen, Q., Huang, D., Wang, Y., & Chen, L. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2557063 · Cited by 97 · see citing works
Automatic 3D facial expression recognition using geometric scattering representation
Yang, X., Huang, D., Wang, Y., & Chen, L. (2015) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2015.7163090 · Cited by 90 · 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
3D/4D facial expression analysis: An advanced annotated face model approach
Fang, T., Zhao, X., Ocegueda, O., Shah, S. K., & Kakadiaris, I. A. (2012) · Image and Vision Computing · doi:10.1016/j.imavis.2012.02.004 · Cited by 81 · see citing works
Recognition of Emotion Intensities Using Machine Learning Algorithms: A Comparative Study
Mehta, D., Siddiqui, M. F. H., & Javaid, A. Y. (2019) · Sensors · doi:10.3390/s19081897 · Cited by 81 · see citing works
3D Approaches and Challenges in Facial Expression Recognition Algorithms—A Literature Review
Nonis, F., Dagnes, N., Marcolin, F., & Vezzetti, E. (2019) · Applied Sciences · doi:10.3390/app9183904 · Cited by 81 · see citing works
Shape analysis of local facial patches for 3D facial expression recognition
Maalej, A., Amor, B. B., Daoudi, M., Srivastava, A., & Berretti, S. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.02.012 · Cited by 80 · 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
Adaptation in Affective Video Games: A Literature Review
Bontchev, B. (2016) · Cybernetics and Information Technologies · doi:10.1515/cait-2016-0032 · Cited by 79 · see citing works
Adaptive 3D facial action intensity estimation and emotion recognition
Zhang, Y., Zhang, L., & Hossain, M. A. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.08.042 · Cited by 76 · 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
Self-citationLemaire, P., Ardabilian, M., Chen, L., & Daoudi, M. (2013) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2013.6553821 · Cited by 67 · see citing works
Zhao, X., Huang, D., Dellandrea, E., & Chen, L. (2010) · 2010 20th International Conference on Pattern Recognition · doi:10.1109/icpr.2010.907 · Cited by 66 · see citing works
Facial Expression Recognition via Joint Deep Learning of RGB-Depth Map Latent Representations
Oyedotun, O. K., Demisse, G., Shabayek, A. E. R., Aouada, D., & Ottersten, B. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.374 · Cited by 61 · see citing works
A Deformable 3-D Facial Expression Model for Dynamic Human Emotional State Recognition
Yun, T., & Guan, L. (2013) · IEEE transactions on circuits and systems for video technology (Print) · doi:10.1109/tcsvt.2012.2203210 · Cited by 60 · see citing works
Emotion recognition from an ensemble of features
Tariq, U., Lin, K.-H., Li, Z., Zhou, X., Wang, Z., Le, V., Huang, T. S., Lv, X., & Han, T. X. (2011) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2011.5771365 · Cited by 57 · see citing works
Self-citationApplication of NSGA-II to feature selection for facial expression recognition
Soyel, H., Tekguc, U., & Demirel, H. (2011) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2011.01.010 · Cited by 55 · see citing works
3D human face description: landmarks measures and geometrical features
Vezzetti, E., & Marcolin, F. (2012) · Image and Vision Computing · doi:10.1016/j.imavis.2012.02.007 · Cited by 54 · see citing works
Towards a robust affect recognition: Automatic facial expression recognition in 3D faces
Azazi, A., Lutfi, S. L., Venkat, I., & Fernández-Martínez, F. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.10.042 · Cited by 53 · see citing works
3D facial expression recognition using kernel methods on Riemannian manifold
Hariri, W., Tabia, H., Farah, N., Benouareth, A., & Declercq, D. (2017) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2017.05.009 · Cited by 49 · see citing works
Optimal feature selection for 3D facial expression recognition using coarse-to-fine classification
Soyel, H., & Demirel, H. (2010) · TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES · doi:10.3906/elk-0908-158 · Cited by 48 · see citing works
An automatic 3D expression recognition framework based on sparse representation of conformal images
Zeng, W., Li, H., Chen, L., Morvan, J.-M., & Gu, X. D. (2013) · 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG) · doi:10.1109/fg.2013.6553749 · Cited by 48 · see citing works
Deep Multi-task Learning to Recognise Subtle Facial Expressions of Mental States
Hu, G., Liu, L., Yuan, Y., Yu, Z., Yang, H., Zhang, Z., Shen, F., Shao, L., Hospedales, T. M., Robertson, N., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01258-8_7 · Cited by 48 · see citing works
Local 3D Shape Analysis for Facial Expression Recognition
Maalej, A., Amor, B. B., Daoudi, M., Srivastava, A., & Berretti, S. (2010) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2010.1003 · Cited by 46 · see citing works
Representation, Analysis, and Recognition of 3D Humans
Berretti, S., Daoudi, M., Turaga, P. K., & Basu, A. (2018) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3182179 · Cited by 46 · see citing works
Feature level analysis for 3D facial expression recognition
Sha, T., Song, M., Bu, J., Chen, C., & Tao, D. (2011) · Neurocomputing · doi:10.1016/j.neucom.2011.01.008 · Cited by 44 · see citing works
Texture and Geometry Scattering Representation-Based Facial Expression Recognition in 2D+3D Videos
Yao, Y., Huang, D., Yang, X., Wang, Y., & Chen, L. (2018) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3131345 · Cited by 44 · see citing works
Wei, X., Li, H., Sun, J. Z., & Chen, L. (2018) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2018.00015 · Cited by 44 · see citing works
Automatic facial expression recognition in real-time from dynamic sequences of 3D face scans
Berretti, S., Bimbo, A. D., & Pala, P. (2013) · The Visual Computer · doi:10.1007/s00371-013-0869-2 · Cited by 43 · see citing works
3D Face Discriminant Analysis Using Gauss-Markov Posterior Marginals
Ocegueda, O., Fang, T., Shah, S. K., & Kakadiaris, I. A. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.126 · Cited by 43 · see citing works
3D Facial Expression Recognition Based on Histograms of Surface Differential Quantities
Li, H., Morvan, J., & Chen, L. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23687-7_44 · Cited by 40 · see citing works
Fully automatic 3D facial expression recognition using a region-based approach
Lemaire, P., Amor, B. B., Ardabilian, M., Chen, L., & Daoudi, M. (2011) · Joint ACM Workshop on Human Gesture and Behavior Understanding (J-HGBU) · doi:10.1145/2072572.2072589 · Cited by 39 · see citing works
Fully automatic 3D facial expression recognition using polytypic multi-block local binary patterns
Li, X., Ruan, Q., Jin, Y., An, G., & Zhao, R. (2014) · Signal Processing · doi:10.1016/j.sigpro.2014.09.033 · Cited by 36 · see citing works
Recognizing Emotions From an Ensemble of Features
Tariq, U., Lin, K.-H., Li, Z., Zhou, X., Wang, Z., Le, V., Huang, T. S., Lv, X., & Han, T. X. (2012) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2012.2194701 · Cited by 36 · see citing works
Self-citationA novel approach to classification of facial expressions from 3D-mesh datasets using modified PCA
Venkatesh, Y. V., Kassim, A. A., & Murthy, O. V. R. (2009) · Pattern Recognition Letters · doi:10.1016/j.patrec.2009.04.007 · Cited by 35 · see citing works
Zhang, L., Tjondronegoro, D., & Chandran, V. (2013) · Image and Vision Computing · doi:10.1016/j.imavis.2013.12.008 · Cited by 33 · see citing works
68 landmarks are efficient for 3D face alignment: what about more?
Jabberi, M., Wali, A., Chaudhuri, B. B., & Alimi, A. M. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14770-x · Cited by 33 · see citing works
FERLrTc: 2D+3D facial expression recognition via low-rank tensor completion
Fu, Y., Ruan, Q., Luo, Z., Jin, Y., An, G., & Wan, J. (2019) · Signal Processing · doi:10.1016/j.sigpro.2019.03.015 · Cited by 31 · see citing works
Facial action unit detection: 3D versus 2D modality
Savran, A., Sankur, B., & Bilge, M. T. (2010) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2010.5543263 · Cited by 31 · see citing works
Communication and Automatic Interpretation of Affect from Facial Expressions
Salah, A. A., Sebe, N., & Gevers, T. (2011) · IGI Global eBooks · doi:10.4018/978-1-61692-892-6.ch008 · Cited by 30 · see citing works
Automatic 2.5-D Facial Landmarking and Emotion Annotation for Social Interaction Assistance
Zhao, X., Zou, J., Li, H., Dellandréa, E., Kakadiaris, I. A., & Chen, L. (2015) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2015.2461131 · Cited by 30 · see citing works
Non-rigid registration based model-free 3D facial expression recognition
Savran, A., & Sankur, B. (2017) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2017.07.005 · Cited by 30 · see citing works
Aly, S. F., Wieckowski, A. T., Abbott, L., White, S. W., & Youssef, A. E. (2015) · International Conference on Biometrics · doi:10.1109/icb.2015.7139081 · Cited by 29 · see citing works
Game-Calibrated and User-Tailored Remote Detection of Stress and Boredom in Games
Bevilacqua, F., Engström, H., & Backlund, P. (2019) · Sensors · doi:10.3390/s19132877 · Cited by 28 · see citing works
Zhao, X., Dellandréa, E., Zou, J., & Chen, L. (2013) · Image and Vision Computing · doi:10.1016/j.imavis.2012.10.001 · Cited by 27 · see citing works
Zhang, L., Tjondronegoro, D., & Chandran, V. (2014) · Image and Vision Computing · doi:10.1016/j.imavis.2014.09.005 · Cited by 27 · see citing works
Taha, B., Hayat, M., Berretti, S., Hatzinakos, D., & Werghi, N. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.2984241 · Cited by 27 · see citing works
Muscular Movement Model Based Automatic 3D Facial Expression Recognition
Zhen, Q., Huang, D., Wang, Y., & Chen, L. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-14445-0_45 · Cited by 27 · see citing works
Human emotional state recognition using real 3D visual features from Gabor library
Tie, Y., & Guan, L. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.08.002 · Cited by 25 · see citing works
Facial emotional classification: from a discrete perspective to a continuous emotional space
Hupont, I., Baldassarri, S., & Cerezo, E. (2012) · Pattern Analysis and Applications · doi:10.1007/s10044-012-0286-6 · Cited by 25 · see citing works
A Visual–Audio-Based Emotion Recognition System Integrating Dimensional Analysis
Tian, J., & She, Y. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3200060 · Cited by 24 · see citing works
Automatic detection of facial actions from 3D data
Savran, A., & Sankur, B. (2009) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2009.5457526 · Cited by 24 · see citing works
Emotion Detection Using Kinect 3D Facial Points
Zhang, Z., Cui, L., Liu, X., & Zhu, T. (2016) · IEEE/WIC/ACM International Conference on Web Intelligence · doi:10.1109/wi.2016.0063 · Cited by 24 · see citing works
Automatic Registration of Vertex Correspondences for 3D Facial Expression Analysis
Rosato, M., Chen, X., & Yin, L. (2008) · IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS) · doi:10.1109/btas.2008.4699369 · Cited by 23 · see citing works
Expressive Maps for 3D Facial Expression Recognition
Ocegueda, O., Fang, T., Shah, S. K., & Kakadiaris, I. A. (2011) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2011.6130397 · Cited by 22 · see citing works
Zhang, L., Tjondronegoro, D., Chandran, V., & Eggink, J. (2015) · Multimedia Tools and Applications · doi:10.1007/s11042-015-2497-5 · Cited by 22 · see citing works
Robust facial action recognition from real-time 3D streams
Tsalakanidou, F., & Malassiotis, S. (2009) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2009.5204281 · Cited by 22 · see citing works
On the simultaneous recognition of identity and expression from BU-3DFE datasets
Venkatesh, Y. V., Kassim, A. A., Yuan, J., & Nguyen, T. D. (2012) · Pattern Recognition Letters · doi:10.1016/j.patrec.2012.05.015 · Cited by 22 · see citing works
Geometry vs. Appearance for Discriminating between Posed and Spontaneous Emotions
Zhang, L., Tjondronegoro, D., & Chandran, V. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24965-5_49 · Cited by 21 · see citing works
Features and fusion for expression recognition — A comparative analysis
Tariq, U., & Huang, T. S. (2012) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2012.6239229 · Cited by 21 · see citing works
Self-citationEmotion Recognition from Non‐Frontal Facial Images
Zheng, W., Tang, H., & Huang, T. S. (2015) · Wiley eBooks · doi:10.1002/9781118910566.ch8 · Cited by 20 · see citing works
Self-citationAU recognition on 3D faces based on an extended statistical facial feature model
Zhao, X., Dellandréa, E., Chen, L., & Samaras, D. (2010) · IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS) · doi:10.1109/btas.2010.5634484 · Cited by 18 · see citing works
A design for integrated face and facial expression recognition
Song, K.-T., & Chen, Y.-W. (2011) · Annual Conference of the IEEE Industrial Electronics Society · doi:10.1109/iecon.2011.6120016 · Cited by 18 · see citing works
Li, H., Sun, J., Wang, D., Xu, Z., & Chen, L. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.03015 · Cited by 18 · see citing works
Resampling Approach to Facial Expression Recognition Using 3D Meshes
Venkatesh, Y. V., Kassim, A. A., & Murthy, O. V. R. (2010) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2010.919 · Cited by 17 · see citing works
Human emotion recognition using real 3D visual features from Gabor library
Tie, Y., & Guan, L. (2010) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp.2010.5662073 · Cited by 17 · see citing works
Using Kinect for Facial Expression Recognition under Varying Poses and Illumination
Malawski, F., Kwolek, B., & Sako, S. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-09912-5_33 · Cited by 17 · see citing works
3-Dimensional facial expression recognition in human using multi-points warping
Agbolade, O., Nazri, A., Yaakob, R., Ghani, A., & Cheah, Y. K. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3153-2 · Cited by 17 · see citing works
Automatic Detection of Emotion Valence on Faces Using Consumer Depth Cameras
Savran, A., Gur, R. C., & Verma, R. (2013) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2013.17 · Cited by 15 · see citing works
Soyel, H., & Demirel, H. (2009) · International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control (ICSCCW) · doi:10.1109/icsccw.2009.5379476 · Cited by 14 · see citing works
Evaluation of Texture and Geometry for Dimensional Facial Expression Recognition
Zhang, L., Tjondronegoro, D., & Chandran, V. (2011) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2011.110 · Cited by 14 · see citing works
3D facial expression recognition using residues
Srivastava, R., & Roy, S. (2009) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2009.5395856 · Cited by 14 · see citing works
Multiple strategies to enhance automatic 3D facial expression recognition
Li, X., Ruan, Q., An, G., Jin, Y., & Zhao, R. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.02.063 · Cited by 13 · see citing works
Affective Human-Humanoid Interaction Through Cognitive Architecture
Infantino, I. (2012) · InTech eBooks · doi:10.5772/25794 · Cited by 12 · see citing works
Fuzzy SVM for 3D facial expression classification using sequential forward feature selection
Zarbakhsh, P., & Demirel, H. (2017) · International Conference on Computational Intelligence and Communication Networks (CICN) · doi:10.1109/cicn.2017.8319371 · Cited by 10 · see citing works
Jiao, Y., Niu, Y., Tran, T. D., & Shi, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.08333 · Cited by 10 · see citing works
Weight-based sparse coding for multi-shot person re-identification
Zheng, Y., Sheng, H., Zhang, B., Zhang, J., & Xiong, Z. (2015) · Science China Information Sciences · doi:10.1007/s11432-015-5404-9 · Cited by 9 · see citing works
Real-time Expression Recognition from Dynamic Sequences of 3D Facial Scans
Berretti, S., Bimbo, A. D., & Pala, P. (2012) · Florence Research (University of Florence) · doi:10.2312/3dor/3dor12/085-092 · Cited by 9 · see citing works
Facial expression analysis across databases
Zhang, Z., Fang, C., & Ding, X. (2011) · International Conference on Multimedia Technology (ICMT) · doi:10.1109/icmt.2011.6001655 · Cited by 8 · see citing works
A remarkable standard for estimating the performance of 3D facial expression features
Li, X., Ruan, Q., & Ming, Y. (2011) · Neurocomputing · doi:10.1016/j.neucom.2011.10.029 · Cited by 8 · see citing works
Methods for facial expression recognition with applications in challenging situations
Huang, X. (2014) · Cited by 7 · see citing works
Sheng, N., Cai, Y., Zhan, C., Qiu, C., Cui, Y., & Gao, X. (2016) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei.2016.7852743 · Cited by 7 · see citing works
Broadbent, L., Emrith, K., Farooq, A. R., Smith, M. L., & Smith, L. (2012) · IEEE International Symposium on Robot and Human Interactive Communication · doi:10.1109/roman.2012.6343799 · Cited by 8 · see citing works
A Feedback Estimation Approach for Therapeutic Facial Training
Dittmar, C., Denzler, J., & Groß, H. (2017) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2017.26 · Cited by 7 · see citing works
Alyushin, M. V., & Kolobashkina, L. V. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.11.007 · Cited by 6 · see citing works
Utilizing 3D flow of points for facial expression recognition
Srivastava, R., & Roy, S. (2012) · Multimedia Tools and Applications · doi:10.1007/s11042-012-1322-7 · Cited by 6 · see citing works
Automatic 3D facial expression recognition based on polytypic Local Binary Pattern
Li, X., Ruan, Q., An, G., & Jin, Y. (2014) · International Conference on Signal Processing · doi:10.1109/icosp.2014.7015161 · Cited by 6 · see citing works
- 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
- Social Sciences › Psychology › Experimental and Cognitive Psychology › 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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