Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions

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

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 · see citing works

Automatic Facial Expression Recognition Using Facial Animation Parameters and Multistream HMMs

Aleksic, P. S., & Katsaggelos, A. K. (2006) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2005.863510 · Cited by 235 · see citing works

Facial expression recognition based on Local Binary Patterns: A comprehensive study

Shan, C., Gong, S., & McOwan, P. W. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2008.08.005 · Cited by 2,300 · see citing works

Joint Fine-Tuning in Deep Neural Networks for Facial Expression Recognition

Jung, H., Lee, S., Yim, J., Park, S., & Kim, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.341 · Cited by 781 · see citing works

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

Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications

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

The first facial expression recognition and analysis challenge

Valstar, M. F., Jiang, B., Mehu, M., Pantic, M., & Scherer, K. (2011) · Face and Gesture 2011 · doi:10.1109/fg.2011.5771374 · Cited by 385 · see citing works

Local binary features for texture classification: Taxonomy and experimental study

Liu, L., Fieguth, P., Guo, Y., Wang, X., & Pietikäinen, M. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2016.08.032 · Cited by 369 · see citing works

Learning Affective Features With a Hybrid Deep Model for Audio–Visual Emotion Recognition

Zhang, S., Zhang, S., Huang, T., Gao, W., & Tian, Q. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2719043 · Cited by 346 · see citing works

Video and Image based Emotion Recognition Challenges in the Wild: EmotiW 2015

Dhall, A., Ramana Murthy, O. V., Goecke, R., Joshi, J., & Gedeon, T. (2015) · Proceedings of the 2015 ACM on International Conference on Multimodal Interaction · doi:10.1145/2818346.2829994 · Cited by 305 · see citing works

Do Deep Neural Networks Learn Facial Action Units When Doing Expression Recognition?

Khorrami, P., Paine, T. L., & Huang, T. S. (2015) · 2015 IEEE International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2015.12 · Cited by 285 · see citing works

Multi-Objective Based Spatio-Temporal Feature Representation Learning Robust to Expression Intensity Variations for Facial Expression Recognition

Kim, D. H., Baddar, W. J., Jang, J., & Ro, Y. M. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2695999 · Cited by 257 · see citing works

Facial Expression Recognition Using Facial Movement Features

Zhang, L., & Tjondronegoro, D. (2011) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.13 · Cited by 253 · see citing works

Video-based emotion recognition in the wild using deep transfer learning and score fusion

Kaya, H., Gürpınar, F., & Salah, A. A. (2017) · Image and Vision Computing · doi:10.1016/j.imavis.2017.01.012 · Cited by 235 · see citing works

From individual to group-level emotion recognition: EmotiW 5.0

Dhall, A., Goecke, R., Ghosh, S., Joshi, J., Hoey, J., & Gedeon, T. (2017) · Proceedings of the 19th ACM International Conference on Multimodal Interaction · doi:10.1145/3136755.3143004 · Cited by 218 · 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

Feature Extraction Based on Co-occurrence of Adjacent Local Binary Patterns

Nosaka, R., Ohkawa, Y., & Fukui, K. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-25346-1_8 · Cited by 192 · see citing works

Generalized Completed Local Binary Patterns for Time-Efficient Steel Surface Defect Classification

Luo, Q., Sun, Y., Li, P., Simpson, O., Tian, L., & He, Y. (2019) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2018.2852918 · Cited by 178 · see citing works

Audio-visual emotion fusion (AVEF): A deep efficient weighted approach

Ma, Y., Hao, Y., Chen, M., Chen, J., Lu, P., & Košir, A. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.06.003 · Cited by 155 · see citing works

A review of multimodal emotion recognition from datasets, preprocessing, features, and fusion methods

Pan, B., Hirota, K., Jia, Z., & Dai, Y. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126866 · Cited by 142 · see citing works

Facial expression recognition using Gabor motion energy filters

Wu, T., Bartlett, M. S., & Movellan, J. R. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops · doi:10.1109/cvprw.2010.5543267 · Cited by 126 · see citing works

Multiple kernel learning for emotion recognition in the wild

Sikka, K., Dykstra, K., Sathyanarayana, S., Littlewort, G., & Bartlett, M. (2013) · Proceedings of the 15th ACM on International conference on multimodal interaction · doi:10.1145/2522848.2531741 · Cited by 119 · see citing works

Recognizing Facial Expressions Using a Shallow Convolutional Neural Network

Miao, S., Xu, H., Han, Z., & Zhu, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2921220 · Cited by 110 · see citing works

Multimodal learning for facial expression recognition

Zhang, W., Zhang, Y., Ma, L., Guan, J., & Gong, S. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.04.012 · Cited by 100 · see citing works

Steel surface defects recognition based on multi-type statistical features and enhanced twin support vector machine

Chu, M., Gong, R., Gao, S., & Zhao, J. (2017) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2017.10.020 · Cited by 97 · see citing works

FIVR: Fine-Grained Incident Video Retrieval

Kordopatis-Zilos, G., Papadopoulos, S., Patras, I., & Kompatsiaris, I. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2905741 · Cited by 78 · see citing works

Facial expression recognition in the wild based on multimodal texture features

Sun, B., Li, L., Zhou, G., & He, J. (2016) · Journal of Electronic Imaging · doi:10.1117/1.jei.25.6.061407 · Cited by 74 · see citing works

Recognizing expressions from face and body gesture by temporal normalized motion and appearance features

Chen, S., Tian, Y., Liu, Q., & Metaxas, D. N. (2013) · Image and Vision Computing · doi:10.1016/j.imavis.2012.06.014 · Cited by 72 · see citing works

Partial least squares regression on grassmannian manifold for emotion recognition

Liu, M., Wang, R., Huang, Z., Shan, S., & Chen, X. (2013) · Proceedings of the 15th ACM on International conference on multimodal interaction · doi:10.1145/2522848.2531738 · Cited by 62 · see citing works

Anti-spoofing in Action: Joint Operation with a Verification System

Chingovska, I., Anjos, A., & Marcel, S. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2013.22 · Cited by 62 · see citing works

Local normal binary patterns for 3D facial action unit detection

Sandbach, G., Zafeiriou, S., & Pantic, M. (2012) · 2012 19th IEEE International Conference on Image Processing · doi:10.1109/icip.2012.6467234 · Cited by 56 · see citing works

A Multimodal Dataset for Mixed Emotion Recognition

Yang, P., Liu, N., Liu, X., Shu, Y., Ji, W., Ren, Z., Sheng, J., Yu, M., Yi, R., Zhang, D., et al. (2024) · Scientific Data · doi:10.1038/s41597-024-03676-4 · Cited by 37 · see citing works

Dynamic facial expression recognition by joint static and multi-time gap transition classification

Dapogny, A., Bailly, K., & Dubuisson, S. (2015) · 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG) · doi:10.1109/fg.2015.7163111 · Cited by 24 · see citing works

Facial expression recognition using a combination of enhanced local binary pattern and pyramid histogram of oriented gradients features extraction

Sharifnejad, M., Shahbahrami, A., Akoushideh, A., & Hassanpour, R. Z. (2021) · IET Image Processing · doi:10.1049/ipr2.12037 · Cited by 19 · see citing works

Multi-scale Facial Expression Recognition Based on Multi-Direction Local Binary Pattern

Xu, S., Xiao, S., Wang, S., Zhang, W., Zeng, F., Yu, Y., & Jiang, S. (2025) · 2025 5th International Conference on Communication Technology and Information Technology (ICCTIT) · doi:10.1109/icctit68197.2025.11406676