From individual to group-level emotion recognition: EmotiW 5.0

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

Zhao, G., & Pietikainen, M. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1110 · Cited by 2,846

Face detection, pose estimation, and landmark localization in the wild

Zhu, X., & Ramanan, D. (2012) · 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/cvpr.2012.6248014 · Cited by 2,171

Supervised Descent Method and Its Applications to Face Alignment

Xiong, X., & la Torre, F. D. (2013) · 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/cvpr.2013.75 · Cited by 1,926

CENTRIST: A Visual Descriptor for Scene Categorization

Wu, J., & Rehg, J. M. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.224 · Cited by 710

Collecting Large, Richly Annotated Facial-Expression Databases from Movies

Dhall, A., Goecke, R., Lucey, S., & Gedeon, T. (2012) · IEEE Multimedia · doi:10.1109/mmul.2012.26 · Cited by 658

Deep Learning for Emotion Recognition on Small Datasets using Transfer Learning

Ng, H.-W., Nguyen, V. D., Vonikakis, V., & Winkler, S. (2015) · Proceedings of the 2015 ACM on International Conference on Multimodal Interaction · doi:10.1145/2818346.2830593 · Cited by 657 · cited 1×

Image based Static Facial Expression Recognition with Multiple Deep Network Learning

Yu, Z., & Zhang, C. (2015) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2818346.2830595 · Cited by 602

Static facial expression analysis in tough conditions: Data, evaluation protocol and benchmark

Dhall, A., Goecke, R., Lucey, S., & Gedeon, T. (2011) · 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops) · doi:10.1109/iccvw.2011.6130508 · Cited by 541 · cited 1×

Combining modality specific deep neural networks for emotion recognition in video

Kahou, S. E., Pal, C., Bouthillier, X., Froumenty, P., Gülçehre, Ç., Memisevic, R., Vincent, P., Courville, A., Bengio, Y., Ferrari, R. C., et al. (2013) · Proceedings of the 15th ACM on International conference on multimodal interaction · doi:10.1145/2522848.2531745 · Cited by 391

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

Local Subclass Constraint for Facial Expression Recognition in the Wild

Luo, Z., Hu, J., & Deng, W. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8545847 · Cited by 12

Role of Group Level Affect to Find the Most Influential Person in Images

Ghosh, S., & Dhall, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11012-3_39 · Cited by 12

Emotion recognition in low-resource settings: An evaluation of automatic feature selection methods

Haider, F., Pollak, S., Albert, P., & Luz, S. F. (2020) · Computer Speech & Language · doi:10.1016/j.csl.2020.101119 · Cited by 12

From Smart to Personal Environment: Integrating Emotion Recognition into Smart Houses

Fedotov, D., Matsuda, Y., & Minker, W. (2019) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomw.2019.8730876 · Cited by 12

Automatic Group Cohesiveness Detection With Multi-modal Features

Zhu, B., Guo, X., Barner, K. E., & Boncelet, C. (2019) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3340555.3355716 · Cited by 12

Looking Beyond a Clever Narrative

Shukla, A., Katti, H., Kankanhalli, M., & Subramanian, R. (2018) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3242969.3242988 · Cited by 12

Implementing the Affective Mechanism for Group Emotion Recognition With a New Graph Convolutional Network Architecture

Wang, X., Zhang, D., & Lee, D.-J. (2023) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2023.3320101 · Cited by 11

Group-level Cohesion Prediction using Deep Learning Models with A Multi-stream Hybrid Network

Dang, T. X., Kim, S.-H., Yang, H.-J., Lee, G.-S., & Vo, T.-H. (2019) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3340555.3355715 · Cited by 11

Your Fellows Matter: Affect Analysis across Subjects in Group Videos

Mou, W., Güneş, H., & Patras, I. (2019) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2019.8756514 · Cited by 12

The decadal perspective of facial emotion processing and Recognition: A survey

Rathour, N., Singh, R., Gehlot, A., Akram, S. V., Thakur, A. K., & Kumar, A. (2022) · Displays · doi:10.1016/j.displa.2022.102330 · Cited by 14

Emotion Recognition of a Group of People in Video Analytics Using Deep Off-the-Shelf Image Embeddings

Tarasov, A. V., & Savchenko, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-11027-7_19 · Cited by 11

Affective Computing Databases: In-Depth Analysis of Systematic Reviews and Surveys

Vaz, P. J., Rodrigues, J. M. F., & Cardoso, P. J. S. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2024.3507289 · Cited by 11

Towards a Robust Group-Level Emotion Recognition via Uncertainty-Aware Learning

Zhu, Q., Mao, Q., Zhang, J., Huang, X., & Zheng, W. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3547994 · Cited by 12

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

A Survey on Factors Affecting Facial Expression Recognition based on Convolutional Neural Networks

Dufourq, E. (2020) · South African Institute of Computer Scientists and Information Technologists · doi:10.1145/3410886.3410891 · Cited by 10

EmotiW 2018: Audio-Video, Student Engagement and Group-Level Affect PredictionPreprint

Dhall, A., Kaur, A., Goecke, R., & Gedeon, T. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.07773 · Cited by 10

Latest trends in emotion recognition methods: case study on emotiw challenge

Naz, H. U., & Ahuja, S. (2020) · International Journal of Advanced Computer Research · doi:10.19101/ijacr.2019.940117 · Cited by 9

Facial Expression Recognition from a Single Face Image Based on Deep Learning and Broad Learning

Bie, M., Xu, H., Gao, Y. J., & Che, X. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/7094539 · Cited by 9

Enhancing Feature Correlation for Bi-Modal Group Emotion Recognition

Liu, N., Fang, Y., & Guo, Y. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00767-6_3 · Cited by 9

A Mini-Survey and Feasibility Study of Deep-Learning-Based Human Activity Recognition from Slight Feature Signals Obtained Using Privacy-Aware Environmental Sensors

Madokoro, H., Nix, S., Woo, H., & Sato, K. (2021) · Applied Sciences · doi:10.3390/app112411807 · Cited by 11

Novel Method for Three-Dimensional Facial Expression Recognition Using Self-Normalizing Neural Networks and Mobile Devices

Hartmann, T. J., Hartmann, J., Friebe-Hoffmann, U., Lato, C., Janni, W. J., & Látó, K. (2022) · Geburtshilfe und Frauenheilkunde · doi:10.1055/a-1866-2943 · Cited by 9

Prediction of Continuous Emotional Measures through Physiological and Visual Data

Joudeh, I. O., Creţu, A.-M., Bouchard, S., & Guimond, S. (2023) · Sensors · doi:10.3390/s23125613 · Cited by 9

A Hybrid Facial Expression Recognition System Based on Recurrent Neural Network

Guo, J.-M., Huang, P., & Chang, L.-Y. (2019) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2019.8909888 · Cited by 9

Deep Multi-Facial Patches Aggregation Network For Facial Expression RecognitionPreprint

Hazourli, A. R., Djeghri, A., Salam, H., & Othmani, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.09298 · Cited by 9

Task-specific speech enhancement and data augmentation for improved multimodal emotion recognition under noisy conditions

Kshirsagar, S., Pendyala, A., & Falk, T. H. (2023) · Frontiers in Computer Science · doi:10.3389/fcomp.2023.1039261 · Cited by 14

Cross-centroid ripple pattern for facial expression recognition

Verma, M., & Vipparthi, S. K. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19364-9 · Cited by 9

Group Affect Prediction Using Multimodal Distributions

Shamsi, S., Singh, B. P., & Wadhwa, M. (2018) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw.2018.00015 · Cited by 8

Affect Recognition Through Scalogram and Multi-Resolution Cochleagram Features

Haider, F., & Luz, S. F. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1761 · Cited by 8

TikTok for good: Creating a diverse emotion expression database

Surabhi, S., Shah, B., Washington, P. Y., Mutlu, O. C., Leblanc, É., Mohite, P., Husic, A., Kline, A., Dunlap, K., McNealis, M., et al. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00279 · Cited by 8

Face and Emotion Recognition with Neural Networks on Mobile Devices: Practical Implementation on Different Platforms

Efremova, N., Patkin, M., & Sokolov, D. (2019) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2019.8756562 · Cited by 8

Multimodal Dialogue Data Collection and Analysis of Annotation Disagreement

Komatani, K., Okada, S., Nishimoto, H., Araki, M., & Nakano, M. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-15-9323-9_17 · Cited by 8

A recent survey on perceived group sentiment analysis

Rathod, B., Vanzara, R., & Pandya, D. (2023) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2023.103988 · Cited by 7

Revolutionizing facial emotion recognition: in-depth analysis of cutting-edge models, methodologies, and datasets

Sarvakar, K., & Rana, K. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00553-w · Cited by 10

A Survey on Deep Learning for Group-Level Emotion Recognition

Huang, X., Hong, X., Mao, Q., Zheng, W., & Dhall, A. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3638859 · Cited by 14

Development of multimodal sentiment recognition and understanding

Tao, J., Fan, C., Lian, Z., Lyu, Z., Shen, Y., & Liang, S. (2024) · Journal of Image and Graphics · doi:10.11834/jig.240017 · Cited by 8

Deep transfer learning in human–robot interaction for cognitive and physical rehabilitation purposes

Ilyas, C. M. A., Rehm, M., Nasrollahi, K., Madadi, Y., Moeslund, T. B., & Seydi, V. (2021) · Pattern Analysis and Applications · doi:10.1007/s10044-021-00988-8 · Cited by 9

StimSeqOnt: An ontology for formal description of multimedia stimuli sequences

Horvat, M. (2020) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.23919/mipro48935.2020.9245268 · Cited by 7

Learning Transferable Compound Expressions from Masked AutoEncoder Pretraining

Qiu, F., Du, H., Zhang, W., Liu, C., Li, L., Guo, T., & Yu, X. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00476 · Cited by 6

Looking Beyond a Clever Narrative: Visual Context and Attention are Primary Drivers of Affect in Video Advertisements

Shukla, A., Katti, H., Kankanhalli, M., & Subramanian, R. (2018) · NOT FOUND REPOSITORY (Indian Institute of Science Bangalore) · doi:10.48550/arxiv.1808.04610 · Cited by 7

A Learning Analytics Model Based on Expression Recognition and Affective Computing: Review of Techniques and Survey of Acceptance

Wang, C., Dai, J., Chen, Y., Zhang, X., & Xu, L. (2022) · doi:10.2991/978-94-6463-012-1_19 · Cited by 6

Facial Expression Recognition Based on Deep Learning: An Overview

Al-Atroshi, S. J. A., & Ali, A. M. (2023) · Iraqi Journal of Science · doi:10.24996/ijs.2023.64.3.32 · Cited by 6

Fusical: Multimodal Fusion for Video Sentiment

Jin, B. T., Abdelrahman, L., Chen, C. K., & Khanzada, A. (2020) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3382507.3417966 · Cited by 6

Sample Self-Revised Network for Cross-Dataset Facial Expression Recognition

Xu, X., Zheng, W., Zong, Y., Lu, C., & Jiang, X. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892500 · Cited by 6

Emotion Recognition using Multimodal Features

Zhao, J., Chen, S., Wang, S., & Jin, Q. (2018) · Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia) · doi:10.1109/aciiasia.2018.8470385 · Cited by 6

Cross-database non-frontal facial expression recognition based on transductive deep transfer learningPreprint

Yan, K., Zheng, W., Zhang, T., Zong, Y., & Cui, Z. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.12774 · Cited by 6

Prediction of Emotional Measures via Electrodermal Activity (EDA) and Electrocardiogram (ECG)

Joudeh, I. O., Creţu, A.-M., Guimond, S., & Bouchard, S. (2022) · International Electronic Conference on Sensors and Applications (ECSA) · doi:10.3390/ecsa-9-13358 · Cited by 5

Photorealistic Facial Synthesis in the Dimensional Affect Space

Kollias, D., Cheng, S., Pantić, M., & Zafeiriou, S. P. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11012-3_36 · Cited by 5

Joint Prediction of Group-Level Emotion and Cohesiveness with Multi-Task Loss

Zou, B., Lin, Z., Wang, H., Wang, Y., Lyu, X., & Xie, H. (2020) · International Conference on Mathematics and Artificial Intelligence (ICMAI) · doi:10.1145/3395260.3395294 · Cited by 5

Unsupervised Learning Facial Parameter Regressor for Action Unit Intensity Estimation via Differentiable Renderer

Song, X., Shi, T., Feng, Z., Song, M., Lin, J. J., Lin, C., Fan, C., & Yuan, Y. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3413955 · Cited by 5

Multimodal End-to-End Group Emotion Recognition using Cross-Modal AttentionPreprint

Evtodienko, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.05890 · Cited by 12

State-of-the-art Multimodal Emotion Recognition: A comprehensive survey and taxonomy

Yazici, A., Kucukyilmaz, T., Dokeroglu, T., Sharipbay, A., Lee, M.-H., & Tyler, B. (2026) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2026.200642 · Cited by 11

Inferring the Mood of a Community From Their Walking Speed

Matthews, O., Sarsenbayeva, Z., Jiang, W., Newn, J., Velloso, E., Clinch, S., & Gonçalves, J. (2018) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3267305.3274759 · 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

Example-driven modeling of portrait bas-relief

Liu, Y., Ji, Z., Zhang, Y., & Xu, G. (2020) · Computer Aided Geometric Design · doi:10.1016/j.cagd.2020.101860 · Cited by 4

A Comprehensive Study on Facial Expression Recognition Techniques using Convolutional Neural Network

Vasudeva, K., & Chandran, S. (2020) · International Conference on Communications and Signal Processing (ICCSP) · doi:10.1109/iccsp48568.2020.9182076 · Cited by 4

D2C-Based Hybrid Network for Predicting Group Cohesion Scores

Dang, T., Yang, H.-J., Lee, G.-S., & Kim, S.-H. (2021) · IEEE Access · doi:10.1109/access.2021.3088340 · Cited by 4

Collection of Multimodal Dialog Data and Analysis of the Result of Annotation of Users’ Interest Level

Araki, M., Tomimasu, S., Nakano, M., Komatani, K., Okada, S., Fujie, S., & Sugiyama, H. (2018) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/598vipg7kbwt · Cited by 4

Multimodal Affective Computing Using Temporal Convolutional Neural Network and Deep Convolutional Neural Networks

Ayoub, I. (2019) · uO Research (University of Ottawa) · doi:10.20381/ruor-23584 · Cited by 4

Predicting Confusion from Eye-Tracking Data with Recurrent Neural NetworksPreprint

Sims, S. D., Putnam, V., & Conati, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.11211 · Cited by 5

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

Group Emotion Recognition Using Fuzzy Emotional Hierarchical Shared Space

Li, C., Huang, W., Yu, X., Yang, Z., & Long, J. (2025) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2025.3609954 · Cited by 3

Joint Multi-Scale Contextual and Noise Suppression for Group Emotion Recognition

Guo, W., Zhu, Q., & Mao, Q. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888975 · Cited by 3

Audio-based emotion recognition using GMM supervector an SVM linear kernel

Tran, D.-S., Yang, H.-J., Kim, S.-H., Lee, G.-S., Do, L.-N., Ho, N.-H., & Nguyen, V. Q. (2018) · International Conference on Machine Learning and Soft Computing (ICMLSC) · doi:10.1145/3184066.3184086 · Cited by 3

Automatisierte Erkennung von Stimme, Sprache und Gesicht

Karaboga, M., Frei, N., Ebbers, F., Rovelli, S., Friedewald, M., & Runge, G. (2022) · vdf Hochschulverlag AG an der ETH Zürich eBooks · doi:10.3218/4141-5 · Cited by 5

Emolysis: A Multimodal Open-Source Group Emotion Analysis and Visualization Toolkit

Ghosh, S., Cai, Z., Gupta, P., Sharma, G., Dhall, A., Hayat, M., & Gedeon, T. (2024) · International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · doi:10.1109/aciiw63320.2024.00023 · Cited by 3

Video-based Emotion Recognition using Aggregated Features and Spatio-temporal Information

Xu, J., Dong, Y., Ma, L., & Bai, H. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8545441 · Cited by 3

Emotion Recognition In Videos For Low-Memory Systems Using Deep-Learning

Hagar, A. F., Abbas, H. M., & Khalil, M. I. (2019) · International Conference on Computer Engineering & Systems (ICCES) · doi:10.1109/icces48960.2019.9068168 · Cited by 3

Classification of FACS-Action Units with CNN Trained from Emotion Labelled Data Sets

Gerardo, P. C., & Menezes, P. J. (2019) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2019.8914238 · Cited by 3

Enhancing real-time facial emotion recognition in classrooms via Attention-ResNet optimization

Wang, H., Wang, C., Xu, H., Bao, C., & Xu, X. (2025) · The Visual Computer · doi:10.1007/s00371-025-04265-1 · Cited by 2

Visual Methods for Sign Language Recognition: A Modality-Based Review

Seddik, B., & Amara, N. E. B. (2021) · Smart sensors, measurement and instrumentation · doi:10.1007/978-3-030-81982-8_9 · Cited by 2

INTELLIGENT EMOTION RECOGNITION SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK TECHNOLOGY

Buryakova, O. S., Reshetnikova, I. V., & Черкесова, Л. В. (2022) · Современные наукоемкие технологии (Modern High Technologies) · doi:10.17513/snt.39307 · Cited by 2

InFER++: Real-World Indian Facial Expression Dataset

Rizvi, S. S. A., Seth, A., Challa, J. S., & Narang, P. (2024) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2024.3443511 · Cited by 3

Adaptive Key Role Guided Hierarchical Relation Inference for Enhanced Group-Level Emotion Recognition

Zhu, Q., Mao, Q., Dong, W., Shao, X., Huang, X., & Zheng, W. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3618148 · Cited by 2

Analysis of Tourists' Nationality Effects on Behavior-based Emotion and Satisfaction Estimation

Matsuda, Y., Fedotov, D., Arakawa, Y., Suwa, H., Minker, W., & Yasumoto, K. (2020) · International Conference on Informatics, Electronics & Vision (ICIEV) · doi:10.1109/icievicivpr48672.2020.9306525 · Cited by 2

Towards Real-Time Contextual Touristic Emotion and Satisfaction Estimation with Wearable Devices

Fedotov, D., Matsuda, Y., Takahashi, Y., Arakawa, Y., Yasumoto, K., & Minker, W. (2019) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomw.2019.8730864 · Cited by 2

Encoding features robust to unseen modes of variation with attentive long short-term memory

Baddar, W. J., & Ro, Y. M. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.107159 · Cited by 2

Using Normal/Abnormal Video Sequence Categorization to Efficient Facial Expression Recognition in the Wild

Abdallah, T. B., Guermazi, R., & Hammami, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-40605-9_43 · Cited by 2

Performance Dependency of Facial Emotion Recognition System on Dropout and Learning Rate

Kavitha., V., & Chetan, H. R. (2020) · International Conference on Intelligent Sustainable Systems (ICISS) · doi:10.1109/iciss49785.2020.9316077 · Cited by 2

Efficient Group-Based Cohesion Prediction in Images Using Facial Descriptors

Gavrikov, I., & Savchenko, A. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-71214-3_12 · Cited by 2

EMERSK -- Explainable Multimodal Emotion Recognition with Situational Knowledge

Palash, M., & Bhargava, B. (2023) · arXiv · doi:10.48550/arxiv.2306.08657 · Cited by 25

Detecting Natural Emotions in Virtual Reality Through Facial Movement Analysis

Tare, T., Rahman, R., Choi, H. Y., Lim, J., Lee, G. E., Lee, S.-M., Cho, C.-Y., & Damevski, K. (2025) · ACM Symposium on Virtual Reality Software and Technology (VRST) · doi:10.1145/3756884.3766013 · Cited by 1

Dynamics of Collective Group Affect: Group-Level Annotations and the Multimodal Modeling of Convergence and Divergence

Prabhu, N. R., Tsfasman, M., Oertel, C., Gerkmann, T., & Lehmann‐Willenbrock, N. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3643752 · Cited by 1

Navigating the Future of Emotion AI: Technical Barriers, Ethical Concerns, and Sustainable Advancements

Mohiddin, S. K., Sharmila, S., & Slimani, K. (2026) · Information systems engineering and management · doi:10.1007/978-3-032-14778-3_10 · Cited by 1

From Emotions to Violence: Multimodal Fine-Grained Behavior Analysis at the 9th ABAW

Kollias, D., Zafeiriou, S. P., Kotsia, I., Slabaugh, G., Senadeera, D. C., Zheng, J., Yadav, K. K. K., Shao, C., & Hu, G. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00006 · Cited by 1

Smile on the Face, Sadness in the Eyes: Bridging the Emotion Gap With a Multimodal Dataset of Eye and Facial Behaviors

Liu, K., Liu, Y., Wei, L., Tang, C., Zhan, Y., Chen, Z., & Chen, Z. (2026) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2026.3673532 · Cited by 2

Group-Level Human Affect Recognition with Multiple Graph Kernel Fusion

Huang, X. (2022) · Lecture notes in operations research · doi:10.1007/978-3-031-15644-1_11 · Cited by 1

FACIAL EMOTION RECOGNITION USING CONVOLUTIONAL NEURAL NETWORK BASED ON THE VISUAL GEOMETRY GROUP-19

Prabaswera, D. R., & Soeparno, H. (2023) · Jurnal TAM (Technology Acceptance Model) · doi:10.56327/jurnaltam.v14i1.1475 · Cited by 1

Children's Expression Recognition Based on Multi-Scale Asymmetric Convolutional Neural Network

Wang, P., Gong, X., Guo, Q., Chang, G., & Du, F. (2024) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2024.0150744 · Cited by 1

MIP-GAF: A MLLM-Annotated Benchmark for Most Important Person Localization and Group Context Understanding

Madan, S., Ghosh, S., Sookha, L. R., Ganaie, M. A., Subramanian, R., Dhall, A., & Gedeon, T. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00150 · Cited by 2

Dynamic Facial Expression Recognition Based on Trained Convolutional Neural Networks

Li, M., & Wang, Z. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-03335-4_19 · Cited by 1

Extreme Pain Detection based on Human Facial Expressions

Ki, M., & Choi, Y. (2020) · Journal of Digital Contents Society · doi:10.9728/dcs.2020.21.2.415 · Cited by 1

Ensemble Network For Ranking Images Based On Visual Appeal

Singh, S., Sánchez, V., & Guha, T. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9054702 · Cited by 1

An In-Depth Review of Deep Facial Expression Recognition: Obstacles, Utilizations, and Prospective Directions

Almsari, R. I. K., Miry, A. H., & Salman, T. M. (2023) · International Research Journal of Innovations in Engineering and Technology · doi:10.47001/irjiet/2023.712014 · Cited by 1

Group Affect Prediction Using Multimodal DistributionsPreprint

Shamsi, S., Rawat, B. P. S., & Wadhwa, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.01216 · Cited by 1

Deep Multi-Facial patches Aggregation Network for Expression Classification from Face ImagesPreprint

Djerghri, A., Hazourli, A. R., & Othmani, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.10305 · Cited by 1

Deep human face analysis and modelling

Storey, G. (2019) · Northumbria Research Link (Northumbria University) · Cited by 1

A Neural Architecture for Detecting Confusion in Eye-tracking DataPreprint

Sims, S., & Conati, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.06434 · Cited by 1

Cross-Dimension Transfer Learning for Video-Based Facial Expression Recognition

Zhong, K., Li, Y., Fang, L., & Chen, P. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-31456-9_20

Deep audio and video emotion detection

Aminbeidokhti, M. (2020) · Espace ÉTS (ETS)

Behavior Analysis for Human by Facial Expression Recognition Using Deep Learning: A Cognitive Study

Punuri, S. B., Kuanar, S. K., & Mishra, T. K. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-0666-3_60

EmotiW 2016: video and group-level emotion recognition challenges

Dhall, A., Goecke, R., Joshi, J., Hoey, J., & Gedeon, T. (2016) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2993148.2997638 · Cited by 148

EmotiW 2018: Audio-Video, Student Engagement and Group-Level Affect PredictionPreprint

Dhall, A., Kaur, A., Goecke, R., & Gedeon, T. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.07773 · Cited by 10

EmotiW 2023: Emotion Recognition in the Wild Challenge

Dhall, A., Singh, M., Goecke, R., Gedeon, T., Zeng, D., Wang, Y., & Ikeda, K. (2023) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3577190.3616545 · Cited by 18

EmotiW 2018

Dhall, A., Kaur, A., Goecke, R., & Gedeon, T. (2018) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3242969.3264993 · Cited by 139

Emotion recognition in the wild

Dhall, A., Goecke, R., Gedeon, T., & Sebe, N. (2016) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-016-0213-z · Cited by 17