FERA 2015 - second Facial Expression Recognition and Analysis challenge

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Facial action coding system

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Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation

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A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions

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Supervised Descent Method and Its Applications to Face Alignment

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Automatic facial expression analysis: a survey

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BP4D-Spontaneous: a high-resolution spontaneous 3D dynamic facial expression database

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The SEMAINE Database: Annotated Multimodal Records of Emotionally Colored Conversations between a Person and a Limited Agent

McKeown, G., Valstar, M., Cowie, R., Pantic, M., & Schroder, M. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.20 · Cited by 665

Facial Action Coding System

(2017) · The SAGE Encyclopedia of Communication Research Methods · doi:10.4135/9781483381411.n178 · Cited by 559

Facial Action Unit Detection using 3D Facial LandmarksPreprint

Hinduja, S., & Canavan, S. J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.08343 · Cited by 3

Exploiting an Oracle That Reports AUC Scores in Machine Learning Contests

Whitehill, J. (2016) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v30i1.10138 · Cited by 2

Real-Time Stress Detection Using Webcam-Based Physiological Signals and Interpretable ML

Elawady, I., & Kavsaoğlu, A. R. (2025) · International Symposium on Innovative Approaches in Smart Technologies (ISAS) · doi:10.1109/isas66241.2025.11101927 · Cited by 2

Web-based visualisation of head pose and facial expressions changes: Monitoring human activity using depth data

Kalliatakis, G., Vidakis, N., & Triantafyllidis, G. A. (2016) · Computer Science and Electronic Engineering Conference · doi:10.1109/ceec.2016.7835887 · Cited by 2

Framework for an embedded emotion assessment system for space science applications

Bălțoiu, A., Petrică, L., Dinculescu, A. C., & Vizitiu, C. (2017) · E-Health and Bioengineering Conference (EHB) · doi:10.1109/ehb.2017.7995363 · Cited by 2

Dynamic Analysis of Automatic Facial Expressions Recognition ‗in the Wild‘ Using Generalized Additive Mixed Models and Significant Zero Crossing of the Derivatives

Dupré, D., Andelic, N., Morrison, G., & McKeown, G. J. (2018) · Electronic workshops in computing · doi:10.14236/ewic/hci2018.1 · Cited by 2

Dynamic Facial Feature Learning by Deep Evolutionary Neural Networks

Zhi, R., Zhou, C., & Li, T. (2019) · Communications in computer and information science · doi:10.1007/978-981-15-1925-3_23 · Cited by 2

Trend-Aware Supervision: On Learning Invariance for Semi-supervised Facial Action Unit Intensity Estimation

Chen, Y., Zhang, J., Wang, T., & Liang, Y. E. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i1.27803 · Cited by 2

Facial Action Unit Detection Based on Transformer and Attention Mechanism

Song, W., Shi, S., & An, G. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87358-5_37 · Cited by 2

Types of Nonverbal Communication [Working Title]

Jiang, X. (2020) · IntechOpen eBooks · doi:10.5772/intechopen.83004 · Cited by 2

Weakly Supervised Learning for Facial Behavior Analysis: A ReviewPreprint

Praveen, R. G., Cardinal, P., & Granger, É. (2024) · doi:10.36227/techrxiv.173091765.50249538/v1 · Cited by 2

A primer on observational measurementPreprint

Girard, J. M., & Cohn, J. F. (2018) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/582cy · Cited by 3

Biosignal recognition for patients identification

Hussain, H., Ibrahim, M. N., Ting, C., Noman, F. M., Hamedi, M., Sh-Hussain, H., & Ninggal, M. T. (2016) · IEEE-EMBS Conference on Biomedical Engineering and Sciences · doi:10.1109/iecbes.2016.7843420 · Cited by 1

A review study of human-affection knowledge on usability engineering

Lakshmi, D., & Ponnusamy, R. (2016) · International Conference on Advances in Human Machine Interaction (HMI) · doi:10.1109/hmi.2016.7449174 · Cited by 1

MRP-Net: A Light Multiple Region Perception Neural Network for Multi-label AU Detection

Tang, Y. S., Chen, S., Zhang, H., Wang, G., & Yang, R. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412515 · Cited by 1

Dual‐attention guided network for facial action unit detection

Song, W., Shi, S., Wu, Y., & An, G. (2022) · IET Image Processing · doi:10.1049/ipr2.12480 · Cited by 1

Supervised dimensionality reduction for multi-dimensional classification

Jia, B.-B., & ZHANG, M. (2023) · Scientia Sinica Informationis · doi:10.1360/ssi-2022-0363 · Cited by 1

MTFNet: Multi-Scale Transformer Framework for Robust Emotion Monitoring in Group Learning Settings

Zhang, Y., Liu, F., Song, J., Zeng, Q., & He, H. X. (2024) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc63619.2025.10848828 · Cited by 1

Facial expression pair matching

Engin, D., Ekenel, H. K., & Bölümü, B. M. (2017) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2017.7960646 · Cited by 1

The 3D Emotion Recognition Using SVM and HoG Features

Savakar, D. G., & Hosur, R. (2020) · International Journal of Image and Graphics · doi:10.1142/s0219467820500199 · Cited by 1

A Comprehensive Investigation on Emotional Detection in Deep Learning

Anand, M., & Babu, S. (2022) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit228111 · Cited by 2

Unsupervised Facial Action Unit Intensity Estimation via Differentiable OptimizationPreprint

Song, X., Shi, T., Shao, T., Yuan, Y., Feng, Z., & Fan, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.05908 · Cited by 1

Impact of Action Unit Occurrence Patterns on DetectionPreprint

Hinduja, S., Canavan, S. J., & Aathreya, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.07982 · Cited by 1

Computational efficient deep neural network with difference attention maps for facial action unit detectionPreprint

Chen, J., Wang, C., Wang, K., & Liu, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.12082 · Cited by 1

Intensity Estimation of the Real-World Facial Expression

Gao, Y., Li, S., & Deng, W. (2016) · Communications in computer and information science · doi:10.1007/978-981-10-3002-4_7

DEVELOPMENT OF ALGORITHMS FOR TRACKING OF MOVEMENT AND CONTROL POINTS ON THE FACE FOR REMOTE DETECTION OF PSYCHO-EMOTIONAL STATES

Znamenskaya, I. A., Koroteeva, E. Y., Shishakov, V. V., Khakhalin, A. V., Кузьмичева, Е. А., Исайчев, С. А., & Chernorizov, A. M. (2017) · Scientific Visualization · doi:10.26583/sv.9.4.04

Literature Survey and Datasets

Poria, S., Hussain, A., & Cambria, E. (2018) · Socio-affective computing · doi:10.1007/978-3-319-95020-4_3

Nonverbal Communication through Facial Expression in Diverse Conditions

Goyani, M. M. (2021) · IntechOpen eBooks · doi:10.5772/intechopen.95109

Design of Multiple Online Interactive Guiding Platform for College English in the Information Technology Environment

Zhang, J. (2021) · International Conference on Communication and Electronics Systems (ICCES) · doi:10.1109/icces51350.2021.9489189

Improving the Generalizability of Speech Emotion Recognition: Methods for Handling Data and Label Variability

Zhang, B. (2018) · Deep Blue (University of Michigan)

Choose Settings Carefully: Comparing Action Unit Detection At Different Settings Using A Large-Scale DatasetPreprint

Bishay, M., Ghoneim, A., Ashraf, M., & Mavadati, M. (2021) · arXiv (Cornell University) · doi:10.17023/6sn6-x651

Affective pose

Yin, Y. (2018) · doi:10.17760/d20316370

Audiovisual Facial Action Unit Recognition Using Feature Level Fusion

Meng, Z., Han, S., Chen, M., & Tong, Y. (2018) · IGI Global eBooks · doi:10.4018/978-1-5225-5204-8.ch024

Deception Detection System with Joint Cross-Attention

Jiang, P., Wang, Z., Li, J., & Wang, Y. (2024) · International Symposium on Signal Processing Systems (SSPS) · doi:10.1145/3665053.3665056

Review on Facial Emotion Recognition Using Deep Learning With Multiple Databases

Prasad, M. H., Swarnalatha, P., Anantharajah, K., & Prabu, S. (2024) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-3719-6.ch018

A Proposal for Deep Learning Based Emotion Identification

Nayak, S., Pandey, M., & Rautaray, S. S. (2024) · International Conference on System Modeling & Advancement in Research Trends (SMART) · doi:10.1109/smart63812.2024.10882469

How Do We Perceive the Intensity of Facial Expressions? The PIFE Dataset for Analysis of Perceived Intensity

Tiuleneva, M., Castano, E., & Niewiadomski, R. (2024) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii63134.2024.00016

FERGI: Automatic Scoring of User Preferences for Text-to-Image Generation from Spontaneous Facial Expression Reaction

Feng, S., Ma, J., & de Sa, V. R. (2025) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg61629.2025.11099285

Weakly Supervised Learning for Facial Affective Behavior Analysis: A Review

Praveen, R. G., Cardinal, P., & Granger, É. (2026) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2026.3677128 · Cited by 4

Reading Faces. Using Hard Multi-Task Metric Learning for Kernel Regression

Nicolle, J. (2016) · theses.fr (ABES) · doi:10.70675/8a22a6caz95d0z4cb8zaeadz043ba01a754d

Construction non supervisée d'un modèle expressif spécifique à la personne

Weber, R. (2017) · theses.fr (ABES) · doi:10.70675/2f458edfz3d99z43cezb849zdf8729c5aad0

Apprentissage neuronal profond pour l'analyse de contenus multimodaux et temporels

Vielzeuf, V. (2019) · theses.fr (ABES) · doi:10.70675/cd4ec871zb372z492fz94bfz25086c5fda4a

Reconnaissance d'états émotionnels par analyse visuelle du visage et apprentissage machine

Lekdioui, K. (2018) · doi:10.70675/3aa8385cz803fz4142zb84ez9ba03c695fb3

Apprentissage Few Shot et méthode d'élagage pour la détection d'émotions sur bases de données restreintes

Cornec, K. L. (2020) · theses.fr (ABES) · doi:10.70675/208af321z8d3bz4b62z99f6z0b296efd57bc

Deep learning for automatic facial expression assessment with application to the expression of pain

Vu, M. T. (2022) · theses.fr (ABES) · doi:10.70675/b3a4930cz2ae5z4f53z9602ze781cb8f524a

Enhancing facial action unit intensity estimation with ordinal regression-enhanced transformer

Xu, R., Su, S., Xie, C., & Chen, J. (2026) · The Visual Computer · doi:10.1007/s00371-026-04557-0

Expert Knowledge-Informed Heatmap Generation for Action Unit Intensity Regression

Ji, Y., Xu, R., Ma, P., Zheng, Y., Min, Y., Yang, Z., & Chen, J. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38401-0_24

Capturing Dynamics: The 4D Facial Expression Intensity Dataset

zesheng, W., Bruckert, A., Lebreton, P., Callet, P. L., Li, Y., & Zhao, G. (2026) · IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr70517.2026.00076

A Deep Learning Perspective on the Origin of Facial Expressions

Breuer, R., & Kimmel, R. (2017) · arXiv · doi:10.48550/arxiv.1705.01842 · Cited by 92

FERA 2017 - Addressing Head Pose in the Third Facial Expression Recognition and Analysis Challenge

Valstar, M., Sánchez, E., Cohn, J. F., Jeni, L. A., Girard, J. M., Zhang, Z., Yin, L., & Pantić, M. (2017) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2017.107 · Cited by 129

FERA 2014 chairs' welcome

Valstar, M., McKeown, G. J., Méhu, M., Yin, L., Pantić, M., & Cohn, J. F. (2015) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2015.7284866 · Cited by 1

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

in the Facial Expression Recognition and Analysis (FERA2015)

Gao, H. (2016)

Pose-Independent Facial Action Unit Intensity Regression Based on Multi-Task Deep Transfer Learning

Zhou, Y., Pi, J., & Shi, B. E. (2017) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2017.112 · Cited by 41