FERA 2015 - second Facial Expression Recognition and Analysis challenge

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

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Are 3D Face Shapes Expressive Enough for Recognising Continuous Emotions and Action Unit Intensities?

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Gaussian Process Domain Experts for Modeling of Facial Affect

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AM-FED+: An Extended Dataset of Naturalistic Facial Expressions Collected in Everyday Settings

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Improving Speech Related Facial Action Unit Recognition by Audiovisual Information Fusion

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Automated Pain Detection from Facial Expressions using FACS: A ReviewPreprint

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Quantifying dynamic facial expressions under naturalistic conditions

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Fusing Multilabel Deep Networks for Facial Action Unit Detection

Bishay, M., & Patras, I. (2017) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2017.86 · Cited by 21

Emotion Expression from Different Angles: A Video Database for Facial Expressions of Actors Shot by a Camera Array

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Automatic Measurement of Head and Facial Movement for Analysis and Detection of Infants’ Positive and Negative Affect

Hammal, Z., Cohn, J. F., Heike, C. L., & Speltz, M. L. (2015) · Frontiers in ICT · doi:10.3389/fict.2015.00021 · Cited by 21

A Novel Feature Selection Method for Video-Based Human Activity Recognition Systems

Siddiqi, M. H., Alruwaili, M., & Ali, A. (2019) · IEEE Access · doi:10.1109/access.2019.2936621 · Cited by 20

Dominant and Complementary Multi-Emotional Facial Expression Recognition Using C-Support Vector Classification

Loob, C., Rasti, P., Lüsi, I., Jacques, J. C. S., Baró, X., Escalera, S., Sapiński, T., Kamińska, D., & Anbarjafari, G. (2017) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2017.106 · Cited by 18

An efficient model-level fusion approach for continuous affect recognition from audiovisual signals

Pei, E., Jiang, D., & Hichem Sahli, B. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.09.037 · Cited by 21

Affect Recognition using Key Frame Selection based on Minimum Sparse Reconstruction

Kayaoğlu, M., & Erdem, Ç. E. (2015) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2818346.2830594 · Cited by 19

Facial Expression Recognition using Hybrid Texture Features based Ensemble Classifier

Arfan, M. (2017) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2017.080660 · Cited by 19

Region of Interest Based Graph Convolution

Zhang, Z., Wang, T., & Yin, L. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3413674 · Cited by 19

Action unit detection in 3D facial videos with application in facial expression retrieval and recognition

Danelakis, A., Theoharis, T., & Pratikakis, I. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-5699-9 · Cited by 19

Copula Ordinal Regression Framework for Joint Estimation of Facial Action Unit Intensity

Walecki, R., Rudovic, O. O., Pavlović, V., & Pantić, M. (2017) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2728534 · Cited by 18

Region-based facial representation for real-time Action Units intensity detection across datasets

Hupont, I., & Chétouani, M. (2017) · Pattern Analysis and Applications · doi:10.1007/s10044-017-0645-4 · Cited by 17

Geometric positions and optical flow based emotion detection using MLP and reduced dimensions

Khan, G., Siddiqi, A., Khan, M. U. G., Wahla, S. Q., & Samyan, S. (2019) · IET Image Processing · doi:10.1049/iet-ipr.2018.5728 · Cited by 18

Handling Data Imbalance in Automatic Facial Action Intensity Estimation

Werner, P., Saxen, F., & Al-Hamadi, A. K. (2015) · British Machine Vision Conference (BMVC) · doi:10.5244/c.29.124 · Cited by 17

Multimodal Detection of Drivers Drowsiness and Distraction

Das, K., Sharak, S., Riani, K., Abouelenien, M., Burzo, M. G., & Papakostas, M. (2021) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3462244.3479890 · Cited by 17

What can head and facial movements convey about positive and negative affect?

Hammal, Z., Cohn, J. F., Heike, C. L., & Speltz, M. L. (2015) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2015.7344584 · Cited by 17

Towards detecting levels of alertness in drivers using multiple modalities

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Current state of science in machine learning methods for automatic infant pain evaluation using facial expression information: study protocol of a systematic review and meta-analysis

Cheng, D., Liu, D., Philpotts, L. L., Turner, D. P., Houle, T. T., Chen, L. L., Zhang, M., Yang, J., Zhang, W., & Deng, H. (2019) · BMJ Open · doi:10.1136/bmjopen-2019-030482 · Cited by 15

Deep Learned Cumulative Attribute Regression

Jaiswal, S., Egede, J., & Valstar, M. (2018) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2018.00113 · Cited by 15

Listen to Your Face: Inferring Facial Action Units from Audio Channel

Meng, Z., Han, S., & Tong, Y. (2017) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2749299 · Cited by 15

Dynamic Probabilistic Graph Convolution for Facial Action Unit Intensity Estimation

Song, T., Cui, Z., Wang, Y., Zheng, W., & Ji, Q. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00481 · Cited by 16

Landmark-Based Facial Feature Construction and Action Unit Intensity Prediction

Ma, J., Li, X., Ren, Y., Yang, R., & Zhao, Q. (2021) · Mathematical Problems in Engineering · doi:10.1155/2021/6623239 · Cited by 14

Unmasking the Devil in the Details: What Works for Deep Facial Action Coding?

Niinuma, K., Jeni, L. A., Ertuğrul, I. Ö., & Cohn, J. F. (2019) · PubMed · Cited by 14

Deep Facial Action Unit Recognition and Intensity Estimation from Partially Labelled Data

Wang, S., Pan, B., Wu, S., & Ji, Q. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2914654 · Cited by 14

Self-supervised learning of Dynamic Representations for Static Images

Song, S., Sánchez, E., Shen, L., & Valstar, M. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412942 · Cited by 14

Face reading from speech — predicting facial action units from audio cues

Ringeval, F., Marchi, E., Méhu, M., Scherer, K. R., & Schuller, B. W. (2015) · Interspeech (USB) · doi:10.21437/interspeech.2015-435 · Cited by 14

A recurrent cycle consistency loss for progressive face-to-face synthesis

Sánchez, E., & Valstar, M. (2020) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg47880.2020.00015 · Cited by 13

A Survey on: Facial Expression Recognition Using Various Deep Learning Techniques

Bhattacharya, S. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-4369-9_59 · Cited by 13

Facial Landmark Localization in Depth Images Using Supervised Ridge Descent

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Variational Gaussian Process Auto-Encoder for Ordinal Prediction of Facial Action Units

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Exploiting Facial Action Unit in Video for Recognizing Depression using Metaheuristic and Neural Networks

Akbar, H., Dewi, S., Rozali, Y. A., Lunanta, L. P., Anwar, N., & Anwar, D. (2021) · doi:10.1109/iccsai53272.2021.9609747 · Cited by 12

Video Emotion Recognition using Hand-Crafted and Deep Learning Features

Xia, X., Liu, J., Yang, T., Jiang, D., Han, W., & Hichem Sahli, B. (2018) · Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia) · doi:10.1109/aciiasia.2018.8470326 · Cited by 11

Robust Real-time Embedded Face Detection Using Field Programmable Gate Arrays (FPGA)

Alahmadi, A., & Qaisar, S. M. (2019) · Advances in Science and Engineering Technology International Conferences (ASET) · doi:10.1109/icaset.2019.8714270 · Cited by 11

Facial Action Unit Analysis through 3D Point Cloud Neural Networks

Reale, M. J., Klinghoffer, B., Church, M., Szmurlo, H., & Yin, L. (2019) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2019.8756610 · Cited by 11

Mobile-Optimized Facial Expression Recognition Techniques

Zarif, N. E., Montazeri, L., Leduc-Primeau, F., & Sawan, M. (2021) · IEEE Access · doi:10.1109/access.2021.3095844 · Cited by 11

Local-global ranking for facial expression intensity estimation

Baltrušaitis, T., Li, L., & Morency, L. (2017) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2017.8273587 · Cited by 11

Facial action unit intensity estimation and feature relevance visualization with random regression forests

Werner, P., Handrich, S., & Al-Hamadi, A. K. (2017) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2017.8273631 · Cited by 10

Continuous affect recognition with weakly supervised learning

Pei, E., Jiang, D., Alioscha‐Perez, M., & Hichem Sahli, B. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-7313-1 · Cited by 10

Conditional Generative Adversarial Net based Feature Extraction along with Scalable Weakly Supervised Clustering for Facial Expression Classification

Chen, Z., Zhang, L., Tang, J., Mao, J., & Sheng, W. (2024) · International Journal of Network Dynamics and Intelligence · doi:10.53941/ijndi.2024.100024 · Cited by 9

A Review of 25 Spontaneous and Dynamic Facial Expression Databases of Basic Emotions

Kim, H., Bian, Y., & Krumhuber, E. G. (2025) · Affective Science · doi:10.1007/s42761-024-00289-3 · Cited by 11

Facial Action Unit Detection with Multilayer Fused Multi-Task and Multi-Label Deep Learning Network

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Unsupervised Adaptation of a Person-Specific Manifold of Facial Expressions

Weber, R., Barrielle, V., Soladié, C., & Séguier, R. (2018) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2807430 · Cited by 9

Generalizing to Unseen Head Poses in Facial Expression Recognition and Action Unit Intensity Estimation

Werner, P., Saxen, F., Al-Hamadi, A. K., & Yu, H. L. (2019) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2019.8756596 · Cited by 9

Facial Emotion Recognition

Maurya, V., Awasthi, M. A., Singh, R. K., Maurya, D., & Dwivedi, M. (2023) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2023.51265 · Cited by 8

Action Unit Analysis for Monitoring Drivers’ Emotional States

Nabipour, M., & Nikan, S. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3413601 · Cited by 8

A Non-Invasive Approach for Facial Action Unit Extraction and Its Application in Pain Detection

Bouazizi, M., Feghoul, K., Wang, S., Yin, Y., & Ohtsuki, T. O. (2025) · Bioengineering · doi:10.3390/bioengineering12020195 · Cited by 10

High dimensional local binary patterns for facial expression recognition in the wild

Radlak, K., & Bogdan Smolka, B. (2016) · IEEE Mediterranean Electrotechnical Conference (MELECON) · doi:10.1109/melcon.2016.7495381 · Cited by 8

Measuring cues for stand-off deception detection based on full-body nonverbal features in body-worn cameras

Bouma, H., Burghouts, G. J., den Hollander, R. J. M., van der Zee, S., Baan, J., ten Hove, J.-M., van Diepen, S., van den Haak, P., & van Rest, J. H. C. (2016) · Optics and Photonics for Counterterrorism, Crime Fighting, and Defence · doi:10.1117/12.2241183 · Cited by 8

An Adaptive Bayesian Source Separation Method for Intensity Estimation of Facial AUs

Mohammadi, M. R., Fatemizadeh, E., & Mahoor, M. H. (2017) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2707484 · Cited by 8

Machine Learning Based Statistical Analysis of Emotion Recognition using Facial Expression

Ali, A., Nasir, J. A., Ahmed, M. M., Naeem, S., Anam, S., Jamal, F., Chesneau, C., Zubair, M., & Anees, M. S. (2020) · RADS Journal of Biological Research & Applied Sciences · doi:10.37962/jbas.v11i1.262 · Cited by 8

Automatic emotion recognition in the wild using an ensemble of static and dynamic representations

Ghazi, M. M., & Ekenel, H. K. (2016) · Proceedings of the 18th ACM International Conference on Multimodal Interaction · doi:10.1145/2993148.2997634 · Cited by 8

Multi-Output Random Forests for Facial Action Unit Detection

Dapogny, A., Bailly, K., & Dubuisson, S. (2017) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2017.25 · Cited by 8

Local binary pattern variants-based adaptive texture features analysis for posed and nonposed facial expression recognition

Sultana, M., Bhatti, N., Javed, S., & Jung, S. K. (2017) · Journal of Electronic Imaging · doi:10.1117/1.jei.26.5.053017 · Cited by 8

Quantified Facial Temporal-Expressiveness Dynamics for Affect Analysis

Uddin, M. T., & Canavan, S. J. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412656 · Cited by 8

Cauchy-Schwarz Regularized AutoencoderPreprint

Tran, L., Pantić, M., & Deisenroth, M. P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.02149 · Cited by 8

Combining Sequential Geometry and Texture Features for Distinguishing Genuine and Deceptive Emotions

Li, L., Baltrušaitis, T., Sun, B., & Morency, L. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.372 · Cited by 8

Faded Smiles? A Largescale Observational Study of Smiling from Adolescence to Old Age

McDuff, D., & Glass, S. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2922341 · Cited by 7

A dense flow-based framework for real-time object registration under compound motion

Yang, S., An, L., Lei, Y., Li, M., Thakoor, N. S., Bhanu, B., & Liu, Y. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.10.015 · Cited by 7

Real Time Face Expression Classification Using Convolutional Neural Network Algorithm

Wati, V., Kusrini, K., & Fatta, H. A. (2019) · International Conference on Information and Communications Technology (ICOIACT) · doi:10.1109/icoiact46704.2019.8938521 · Cited by 7

FaceCept3D: Real Time 3D Face Tracking and Analysis

Tulyakov, S., Vieriu, R.-L., Sangineto, E., & Sebe, N. (2015) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2015.13 · Cited by 7

An Ensemble Learning Approach for Facial Emotion Recognition Based on Deep Learning Techniques

Almubarak, M., & Alsulaiman, F. A. (2025) · Electronics · doi:10.3390/electronics14173415 · Cited by 8

Multi-dimensional classification: paradigm, algorithms and beyond

Jia, B.-B., & Zhang, M.-L. (2024) · Vicinagearth. · doi:10.1007/s44336-024-00004-7 · Cited by 6

Audiovisual Facial Action Unit Recognition using Feature Level Fusion

Meng, Z., Han, S., Chen, M., & Tong, Y. (2016) · International Journal of Multimedia Data Engineering and Management · doi:10.4018/ijmdem.2016010104 · Cited by 6

Multi-Label Action Unit Detection on Multiple Head Poses with Dynamic Region Learning

Albiero, V., Bellon, O. R. P., & da Silva, L. T. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451267 · Cited by 6

A Simple Yet Effective Convolutional Neural Network Model to Classify Facial Expressions

Sari, M., Moussaouı, A., & Hadid, A. (2020) · Lecture notes in networks and systems · doi:10.1007/978-3-030-58861-8_14 · Cited by 6

Region Attentive Action Unit Intensity Estimation With Uncertainty Weighted Multi-Task Learning

Chen, H., Jiang, D., Zhao, Y. H., Wei, X.-Y., Lü, K., & Hichem Sahli, B. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3139101 · Cited by 6

Reconstructing Facial Expressions of HMD Users for Avatars in VR

Purps, C. F., Janzer, S., & Wölfel, M. (2022) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-95531-1_5 · Cited by 7

Speaker-Independent Multimodal Sentiment Analysis for Big Data

Cambria, E., Poria, S., & Hussain, A. (2019) · Springer eBooks · doi:10.1007/978-3-319-97598-6_2 · Cited by 6

GFT Facial Expression DatabasePreprint

Girard, J. M., Chu, W.-S., Jeni, L. A., Cohn, J. F., de la Torre, F. N., & Sayette, M. A. (2018) · doi:10.31234/osf.io/qfzsm · Cited by 6

Semi-supervised Facial Action Unit Intensity Estimation with Contrastive Learning

Sánchez, E., Bulat, A., Zaganidis, A., & Tzimiropoulos, G. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-69541-5_7 · Cited by 5

Modelling Stochastic Context of Audio-Visual Expressive Behaviour With Affective Processes

Tellamekala, M. K., Giesbrecht, T., & Valstar, M. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3157141 · Cited by 5

Modeling head motion entrainment for prediction of couples' behavioral characteristics

Xiao, B. J., Georgiou, P., Baucom, B., & Narayanan, S. S. (2015) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2015.7344556 · Cited by 5

Challenges in Deep Learning for Multimodal Applications

Ghosh, S. (2015) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2818346.2823313 · Cited by 5

Feature Level Fusion for Bimodal Facial Action Unit Recognition

Meng, Z., Han, S., Chen, M., & Tong, Y. (2015) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2015.116 · Cited by 5

A Framework for Joint Estimation and Guided Annotation of Facial Action Unit Intensity

Walecki, R., Rudovic, O. O., Pantić, M., Pavlović, V., & Cohn, J. F. (2016) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2016.183 · Cited by 5

A joint dictionary learning and regression model for intensity estimation of facial AUs

Mohammadi, M. R., Fatemizadeh, E., & Mahoor, M. H. (2017) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2017.05.002 · Cited by 5

Face Analysis in the Wild

Zavan, F. H. D. B., Gasparin, N., Batista, J. C., Silva, L. P. E., Albiero, V., Bellon, O. R. P., & da Silva, L. T. (2017) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi-t.2017.11 · Cited by 5

Edge Convolutional Network for Facial Action Intensity Estimation

Li, L., Baltrušaitis, T., Sun, B., & Morency, L. (2018) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2018.00034 · Cited by 5

Joint Facial Action Unit Intensity Prediction And Region Localisation

Fan, Y., Shen, J., Cheng, H., & Tian, F. (2020) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme46284.2020.9102833 · Cited by 5

Analysis of ECG biosignal recognition for client identifiction

Hussain, H., Ting, C., Noman, F. M., Ibrahim, M. N., Izan, N. F., Mohammad, M., & Sh-Hussain, H. (2017) · IEEE International Conference on Signal and Image Processing Applications (ICSIPA) · doi:10.1109/icsipa.2017.8120572 · Cited by 5

Lightweight network architecture using difference saliency maps for facial action unit detection

Jing, C., Wang, C., Wang, K., & Liu, M. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02755-y · Cited by 5

Expression recognition using semantic information and local texture features

Wu, C., Huang, C., & Chen, H. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-5158-z · Cited by 4

Facial Representation for Automatic Facial Action Unit Analysis System

Zhi, R., Liu, M., & Zhang, D. (2019) · IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC) · doi:10.1109/itaic.2019.8785870 · Cited by 4

Audio-Visual Emotion Recognition with Capsule-like Feature Representation and Model-Based Reinforcement Learning

Ouyang, X., Nagisetty, S., Goh, E. G. H., Shen, S., Ding, W., Ming, H., & Huang, D. (2018) · Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia) · doi:10.1109/aciiasia.2018.8470316 · Cited by 4

Inferring Dynamic Representations of Facial Actions from a Still ImagePreprint

Song, S., Sánchez, E., Shen, L., Johnston, A., & Valstar, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.02382 · Cited by 4

Graph-based Facial Affect Analysis: A Review of Methods, Applications and Challenges.Preprint

Liu, Y. M., Zhang, X., Zhou, J., Li, X., Li, Y., & Zhao, G. (2021) · arXiv (Cornell University) · Cited by 4

Enhanced facial action unit detection with adaptable patch sizes on representative landmarks

Çakır, D., Yılmaz, G., & Arıca, N. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10836-5 · Cited by 3

Semi-Supervised Deep Neural Network for Joint Intensity Estimation of Multiple Facial Action Units

Zhang, Y., Fan, Y., Dong, W., Hu, B.-G., & Ji, Q. (2019) · IEEE Access · doi:10.1109/access.2019.2947201 · Cited by 3

A benchmark of dynamic versus static methods for facial action unit detection

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in the Facial Expression Recognition and Analysis (FERA2015)

Gao, H. (2016)

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