The DOI matches its Crossref record, with a small difference in title or year; 69 of the 69 references it cites were found in a registry or an index.
Deep Pain: Exploiting Long Short-Term Memory Networks for Facial Expression Classification
Open accessAbstract
Pain is an unpleasant feeling that has been shown to be an important factor for the recovery of patients. Since this is costly in human resources and difficult to do objectively, there is the need for automatic systems to measure it. In this paper, contrary to current state-of-the-art techniques in pain assessment, which are based on facial features only, we suggest that the performance can be enhanced by feeding the raw frames to deep learning models, outperforming the latest state-of-the-art results while also directly facing the problem of imbalanced data. As a baseline, our approach first uses convolutional neural networks (CNNs) to learn facial features from VGG_Faces, which are then linked to a long short-term memory to exploit the temporal relation between video frames. We further compare the performances of using the so popular schema based on the canonically normalized appearance versus taking into account the whole image. As a result, we outperform current state-of-the-art area under the curve performance in the UNBC-McMaster Shoulder Pain Expression Archive Database. In addition, to evaluate the generalization properties of our proposed methodology on facial motion recognition, we also report competitive results in the Cohn Kanade+ facial expression database.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“More advanced face frontalization involving the approximation of 3D shape is useful when dealing with 3D head pose variation [104], [105].”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [105]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891
Learning Deep Architectures for AI
Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 7,330
3D Convolutional Neural Networks for Human Action Recognition
Ji, S., Xu, W., Yang, M., & Yu, K. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.59 · Cited by 6,313
Flagged first, then by citations
Research works printed but not linked: 1
- Ekman, 2002, Facial Action Coding System—The Manual on CD-ROM
Cited by
Sorted by citations
Items 101–200 of 297
GCANet: Geometry cues-aware facial expression recognition based on graph convolutional networks
Wang, S., Zhao, A., Lai, C., Zhang, Q., Li, D., Gao, Y., Dong, L., & Wang, X. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101605 · Cited by 22
Spatio-Temporal Pain Estimation Network With Measuring Pseudo Heart Rate Gain
Huang, D. J., Feng, X., Zhang, H., Yu, Z., Peng, J., Zhao, G., & Xia, Z. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3096080 · Cited by 21
Kosch, T., Hassib, M. E., Reutter, R., & Alt, F. (2020) · International Conference on Advanced Visual Interfaces (AVI) · doi:10.1145/3399715.3399928 · Cited by 21
Tan, C. W., Du, T., Teo, J. C., Chan, D. X. H., Kong, W. M., & Sng, B. L. (2025) · Scientific Reports · doi:10.1038/s41598-025-97885-5 · Cited by 23
A Multimodal Approach for Predicting Changes in PTSD Symptom Severity
Mallol-Ragolta, A., Dhamija, S., & Boult, T. E. (2018) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3242969.3242981 · Cited by 20
PAU-Net: Privileged Action Unit Network for Facial Expression Recognition
Wang, X., Zhang, T., & Chen, C. L. P. (2022) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2022.3203822 · Cited by 20
Dynamic Facial Expression Recognition Based on Convolutional Neural Networks with Dense Connections
Dong, J., Zheng, H., & Lian, L. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8545596 · Cited by 19
Detecting discomfort in infants through facial expressions
Sun, Y., Shan, C., Tan, T., Tong, T., Wang, W., Pourtaherian, A., & de With, P. H. N. (2019) · Physiological Measurement · doi:10.1088/1361-6579/ab55b3 · Cited by 20
Expression recognition with deep features extracted from holistic and part-based models
Happy, S. L., Dantcheva, A., & Brémond, F. (2020) · Image and Vision Computing · doi:10.1016/j.imavis.2020.104038 · Cited by 20
A Personalized Spatial-Temporal Cold Pain Intensity Estimation Model Based on Facial Expression
Guo, Y., Wang, L., Xiao, Y., & Lin, Y. (2021) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2021.3116867 · Cited by 20
Recurrent Neural Network-Based Stencil Cleaning Cycle Predictive Modeling
Wang, H., He, T., & Yoon, S. W. (2018) · Procedia Manufacturing · doi:10.1016/j.promfg.2018.10.017 · Cited by 19
Deep Temporal–Spatial Aggregation for Video-Based Facial Expression Recognition
Pan, X., Guo, W., Guo, X., Li, W., Xu, J., & Wu, J. (2019) · Symmetry · doi:10.3390/sym11010052 · Cited by 19
Real-time pain detection in facial expressions for health robotics
Dai, L., Broekens, J., & Truong, K. P. (2019) · International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · doi:10.1109/aciiw.2019.8925192 · Cited by 19
Detection of Concrete Cracks Using Dual-channel Deep Convolutional Network
Kumar, B. G. V., & Ghosh, S. (2020) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt49239.2020.9225391 · Cited by 19
Performance Evaluation of Keyword Extraction Methods and Visualization for Student Online Comments
Liu, F., Huang, X., Huang, W., & Duan, S. X. (2020) · Symmetry · doi:10.3390/sym12111923 · Cited by 23
Vision-Based Mouth Motion Analysis in Epilepsy: A 3D Perspective
Ahmedt-Aristizabal, D., Nguyen, K., Denman, S., Sarfraz, M. S., Sridharan, S., Dionisio, S., & Fookes, C. (2019) · 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8857656 · Cited by 17
Convolution Neural Network for Pain Intensity Assessment from Facial Expression
Hosseini, E., Fang, R., Zhang, R., Chuah, C., Orooji, M., Rafatirad, S., Rafatirad, S., & Homayoun, H. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871770 · Cited by 18
Enforcing Multilabel Consistency for Automatic Spatio-Temporal Assessment of Shoulder Pain Intensity
Erekat, D., Hammal, Z., Siddiqui, M., & Dibeklioğlu, H. (2020) · Companion Publication of the International Conference on Multimodal Interaction (ICMI Companion) · doi:10.1145/3395035.3425190 · Cited by 18
Casti, P., Mencattini, A., Filippi, J., D’Orazio, M., Comes, M. C., Giuseppe, D. D., & Martinelli, E. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3067611 · Cited by 17
Personalized Federated Deep Learning for Pain Estimation From Face ImagesPreprint
Rudovic, O. O., Tobis, N., Kaltwang, S., Schuller, B. W., Rueckert, D., Cohn, J. F., & Picard, R. W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.04800 · Cited by 27
Ebenezar, U. S., Manikandan, S. P., Senthilvel, P. G., & Sivasankar, C. (2025) · Robotica · doi:10.1017/s0263574724002224 · Cited by 17
Bioelectronics for targeted pain management
Flavin, M. T., Foppiani, J., Paul, M. A., Alvarez, A. H., Foster, L., Gavlasova, D., Ma, H., Rogers, J. A., & Lin, S. J. (2025) · Nature Reviews Electrical Engineering · doi:10.1038/s44287-025-00177-3 · Cited by 18
Pain expression assessment based on a locality and identity aware network
Xin, X., Li, X., Yang, S.-F., Lin, X., & Zheng, X. (2021) · IET Image Processing · doi:10.1049/ipr2.12282 · Cited by 16
Rashidi, P., Edwards, D. A., & Tighe, P. J. (2019) · Current Opinion in Anaesthesiology · doi:10.1097/aco.0000000000000779 · Cited by 16
Neuromorphic Event-based Face Identity Recognition
Moreira, G., Graca, A., Silva, B., Martins, P., & Batista, J. P. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956236 · Cited by 15
Local and correlation attention learning for subtle facial expression recognition
Wang, S., Yuan, Y., Zheng, X., & Lu, X. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.07.120 · Cited by 15
Pain Intensity Recognition from Masked Facial Expressions using Swin-Transformer
Yuan, X., Zhang, S., Zhao, C., He, X., Ouyang, B., & Yang, S. (2022) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio55434.2022.10011731 · Cited by 15
Szczapa, B., Daoudi, M., Berretti, S., Pala, P., Bimbo, A. D., & Hammal, Z. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3207001 · Cited by 14
Faces of Experimental Pain: Transferability of Deep-Learned Heat Pain Features to Electrical Pain*
Prajod, P., Schiller, D., Don, D. S. W., & André, E. (2024) · International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · doi:10.1109/aciiw63320.2024.00009 · Cited by 14
Umapathy, S., & Krishnan, P. T. (2021) · Expert Systems · doi:10.1111/exsy.12747 · Cited by 14
Incorporating high-level and low-level cues for pain intensity estimation
Yang, R., Hong, X., Peng, J., Feng, X., & Zhao, G. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8545244 · Cited by 14
Multimodal-Based Stream Integrated Neural Networks for Pain Assessment
Zhi, R., Zhou, C., Junwei, Y. U., Li, T., & Zamzmi, G. (2021) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2021edp7065 · Cited by 13
Wu, J., Shi, Y., Tang, Y., Ren, W., & Yan, H. (2024) · Journal of Electronic Science and Technology · doi:10.1016/j.jnlest.2024.100260 · Cited by 13
MVFNet: A multi-view fusion network for pain intensity assessment in unconstrained environment
Semwal, A., & Londhe, N. D. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102537 · Cited by 13
Prajod, P., Schiller, D., Huber, T., & André, E. (2022) · Studies in computational intelligence · doi:10.1007/978-3-030-93080-6_16 · Cited by 14
Review and Analysis of Patients’ Body Language From an Artificial Intelligence Perspective
Turaev, S., Al-Dabet, S., Babu, A., Rustamov, Z., Rustamov, J., Zaki, N., Mohamad, M. S., & Loo, C. K. (2023) · IEEE Access · doi:10.1109/access.2023.3287788 · Cited by 12
System Evolution Analytics: Deep Evolution and Change Learning of Inter-Connected Entities
Chaturvedi, A., & Tiwari, A. (2018) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2018.00657 · Cited by 12
A Curriculum Learning Approach for Pain Intensity Recognition from Facial Expressions
Mallol-Ragolta, A., Liu, S., Cummins, N., & Schuller, B. W. (2020) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg47880.2020.00083 · Cited by 13
Mathematical model and artificial intelligence for diagnosis of Alzheimer’s disease
Davodabadi, A., Daneshian, B., Saati, S., & Razavyan, S. (2023) · The European Physical Journal Plus · doi:10.1140/epjp/s13360-023-04128-5 · Cited by 15
Facial Expressions of Comprehension (FEC)
Turan, C., Neergaard, K. D., & Lam, K. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2954498 · Cited by 13
Photogram Classification-Based Emotion Recognition
López, J. M., & Garay-Vitoria, N. (2021) · IEEE Access · doi:10.1109/access.2021.3117253 · Cited by 12
Yue, J.-M., Wang, Q., Liu, B., & Zhou, L. (2024) · World Journal of Clinical Cases · doi:10.12998/wjcc.v12.i4.681 · Cited by 11
PLAAN: Pain Level Assessment with Anomaly-detection based Network
Li, Y., Ghosh, S., & Joshi, J. (2021) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-020-00362-8 · Cited by 12
Pain detection from facial expressions using domain adaptation technique
Rathee, N., Pahal, S., & Sheoran, P. (2021) · Pattern Analysis and Applications · doi:10.1007/s10044-021-01025-4 · Cited by 11
Early Diagnosis of ASD based on Facial Expression Recognition and Head Pose Estimation
Song, C., Li, J., & Ouyang, G. (2022) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53654.2022.9945448 · Cited by 11
Pain intensity recognition via multi‐scale deep network
Peng, X., Huang, D. J., & Zhang, H. (2020) · IET Image Processing · doi:10.1049/iet-ipr.2019.1448 · Cited by 11
Pain detection using batch normalized discriminant restricted Boltzmann machine layers
Kharghanian, R., Peiravi, A., Moradi, F., & Iosifidis, A. (2021) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2021.103062 · Cited by 11
Semwal, A., & Londhe, N. D. (2021) · International Conference on Cloud Computing, Data Science & Engineering (Confluence) · doi:10.1109/confluence51648.2021.9377197 · Cited by 11
SAFEPA: An Expandable Multi-Pose Facial Expressions Pain Assessment Method
Alghamdi, T., & Alaghband, G. (2023) · Applied Sciences · doi:10.3390/app13127206 · Cited by 13
Automated Facial Expression based Pain Assessment Using Deep Convolutional Neural Network
Semwal, A., & Londhe, N. D. (2020) · International Conference on Intelligent Sustainable Systems (ICISS) · doi:10.1109/iciss49785.2020.9316099 · Cited by 10
A Deep Attention Transformer Network for Pain Estimation with Facial Expression Video
Xu, H., & Liu, M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86608-2_13 · Cited by 10
Xiang, X., Wang, F., Tan, Y., & Yuille, A. (2022) · Pattern Recognition Letters · doi:10.1016/j.patrec.2022.09.022 · Cited by 10
Combining Deep and Hand-Crafted Features for Audio-Based Pain Intensity Classification
Thiam, P., & Schwenker, F. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20984-1_5 · Cited by 12
Automated Pain Severity Detection Using Convolutional Neural Network
Semwal, A., & Londhe, N. D. (2018) · International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS) · doi:10.1109/ctems.2018.8769123 · Cited by 10
Frame by Frame Pain Estimation Using Locally Spatial Attention Learning
Yu, J., Kurihara, T., & Zhan, S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-31321-0_20 · Cited by 10
(2020) · The International Arab Journal of Information Technology · doi:10.34028/iajit/18/1/14 · Cited by 10
Non-contact Pain Recognition from Video Sequences with Remote Physiological Measurements Prediction
Yang, R., Guan, Z., Yu, Z., Feng, X., Peng, J., & Zhao, G. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/170 · Cited by 10
Occluded Facial Pain Assessment in the ICU Using Action Units Guided Network
Yuan, X., Cui, Z., Xu, D., Zhang, S., Zhao, C., Wu, X., Jia, T., & Ouyang, B. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3336157 · Cited by 10
AI Based Acute Pain Detection: Insights Through Facial Expression Analysis Using CNN
Dharmendra, D., Benaka Santhosha, S., Vishwas, D. B., Santhosh, M., & Deepak, H. (2024) · International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) · doi:10.1109/icses63760.2024.10910760 · Cited by 9
Multi-Scale Regional Attention Networks for Pain Estimation
Cui, S., Huang, D. J., Ni, Y., & Feng, X. (2021) · International Conference on Bioinformatics and Biomedical Technology (ICBBT) · doi:10.1145/3473258.3473259 · Cited by 9
Image-Based Pain Intensity Estimation Using Parallel CNNs with Regional Attention
Ye, X., Liang, X., Hu, J., & Xie, Y. (2022) · Bioengineering · doi:10.3390/bioengineering9120804 · Cited by 9
Pain estimation with integrating global‐wise and region‐wise convolutional networks
Huang, D. J., Xia, Z., Li, L., & Ma, Y. (2022) · IET Image Processing · doi:10.1049/ipr2.12639 · Cited by 9
Fixation Prediction and Visual Priority Maps for Biped Locomotion
Anantrasirichai, N., Daniels, K., Burn, J. F., Gilchrist, I. D., & Bull, D. (2017) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2734946 · Cited by 9
Comparing Methods for Assessment of Facial Dynamics in Patients with Major Neurocognitive Disorders
Wang, Y., Dantcheva, A., Broutart, J.-C., Robert, P. H., Brémond, F., & Biliński, P. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11024-6_10 · Cited by 9
Wang, H., Won, D., & Yoon, S. W. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107686 · Cited by 9
A cascaded spatiotemporal attention network for dynamic facial expression recognition
Ye, Y., Pan, Y., Liang, Y., & Pan, J. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03781-0 · Cited by 9
Tang, C., Zhang, D., & Tian, Q. (2023) · Applied Sciences · doi:10.3390/app132212418 · Cited by 9
Evaluating neonatal pain via fusing vision transformer and concept-cognitive computing
Lin, J., Zhang, L., Xia, J., & Zhang, Y. (2024) · Scientific Reports · doi:10.1038/s41598-024-77521-4 · Cited by 8
EarthView: A Large Scale Remote Sensing Dataset for Self-Supervision
Velazquez, D. A., Rodríguez, P., Alonso, S., Gonfaus, J. M., Gonzàlez, J., Richarte, G., Marín, J., Bengio, Y., & Lacoste, A. (2025) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw65960.2025.00136 · Cited by 12
Self-citationMulti-modal Fusion Based Automatic Pain Assessment
Zhi, R., & Yu, J. (2019) · IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC) · doi:10.1109/itaic.2019.8785727 · Cited by 8
How Much Does It Hurt: A Deep Learning Framework for Chronic Pain Score Assessment
Zhao, Y., Ly, F. S., Hong, Q., Cheng, Z., Santander, T., Yang, H. T. Y., Hansma, P. K., & Petzold, L. R. (2020) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw51313.2020.00092 · Cited by 8
DeepDriver: Automated System For measuring Valence and Arousal in Car Driver Videos
Theagarajan, R., Bhanu, B., & Cruz, A. C. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8546284 · Cited by 8
A multi-stream spatio-temporal network based behavioural multiparametric pain assessment system
Semwal, A., & Londhe, N. D. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105820 · Cited by 7
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
Self-citationAutomated Pain Estimation based on Facial Action Units from Multi-Databases
Vu, M. T., Beurton‐Aimar, M., Dezaunay, P.-Y., & Eslous, M. C. (2021) · International Conference on Informatics, Electronics & Vision (ICIEV) · doi:10.1109/icievicivpr52578.2021.9564244 · Cited by 7
Rehabilitation of Traumatic Brain Injured Patients: Patient Mood Analysis from Multimodal Video
Ilyas, C. M. A., Nasrollahi, K., Rehm, M., & Moeslund, T. B. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451223 · Cited by 7
Self-citationIVAS: Facilitating Safe and Comfortable Driving with Intelligent Vehicle Audio Systems
Li, T., Kaewtip, K., Feng, J., & Lin, L. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8622595 · Cited by 7
LSTM-DNN based Approach for Pain Intensity and Protective Behaviour Prediction
Li, Y., Ghosh, S., Joshi, J., & Oviatt, S. L. (2020) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg47880.2020.00061 · Cited by 7
Nicolò, V. D., & Torre, D. L. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13671-9.00004-1 · Cited by 6
A Generative Adversarial Network-Based Approach for Facial Pain Assessment
Wang, L., Wang, Z., Xu, A., & Liu, S. (2024) · International Conference on Biomedical Engineering and Applications (ICBEA) · doi:10.1109/icbea62825.2024.00017 · Cited by 6
A Personalized Assessment Platform for Non-invasive Monitoring of Pain
Casti, P., Mencattini, A., Filippi, J., D’Orazio, M., Comes, M. C., Giuseppe, D. D., & Martinelli, E. (2020) · IEEE International Symposium on Medical Measurements and Applications (MeMeA) · doi:10.1109/memea49120.2020.9137138 · Cited by 6
Fused deep learning based Facial Expression Recognition of students in online learning mode
Sumalakshmi, C., & Vasuki, P. (2022) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7137 · Cited by 7
Facial Expressions of Sentence Comprehension
Turan, C., Wang, Y., Lai, S.-C., Neergaard, K. D., & Lam, K. (2018) · IEEE International Conference on Digital Signal Processing (DSP) · doi:10.1109/icdsp.2018.8631633 · Cited by 6
A Sequential Approach for Pain Recognition Based on Facial Representations
Mauricio, A., Cappabianco, F. A. M., Veloso, A., & Cámara-Chávez, G. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34995-0_27 · Cited by 6
Deep Learning in Biometric Authentication: Challenges, Recent Advancements, and Future Trends
Alduhailan, A., Kamarudin, N. H., Abdullah, S. N. H. S., & Da’u, A. (2025) · Journal of Advances in Information Technology · doi:10.12720/jait.16.4.458-477 · Cited by 5
Cascella, M., Esposito, D., Muzio, M. R., Cascella, V., & Cerrone, V. (2025) · Exploration of Medicine · doi:10.37349/emed.2025.1001370 · Cited by 5
Alphonse, S., Abinaya, S., & Kumar, N., V (2024) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-024-00706-9 · Cited by 5
Morsali, M., & Ghaffari, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-04552-w · Cited by 5
Dynamics Are Important for the Recognition of Equine Pain in Video
Broomé, S., Gleerup, K. B., Andersen, P. H., & Kjellström, H. (2019) · 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.2019.01295 · Cited by 8
Neonatal Pain Assessment From Facial Expression Using Deep Neural Networks
Buzuti, L. F., Heiderich, T. M., Barros, M., Guinsburg, R., & Thomaz, C. E. (2020) · Workshop of Computer Vision (WVC) · doi:10.5753/wvc.2020.13486 · Cited by 5
Performance of Deep Learning Benchmark Models on Thermal Imagery of Pain through Facial Expressions
Islamadina, R., Saddami, K., Oktiana, M., Abidin, T. F., Muharar, R., & Arnia, F. (2022) · IEEE International Conference on Communication, Networks and Satellite (COMNETSAT) · doi:10.1109/comnetsat56033.2022.9994546 · Cited by 5
Pain Assessment Model using Facial Recognition
Swetha, L., Praiscia, A., & Juliet, S. (2022) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iciccs53718.2022.9788265 · Cited by 5
Deep Spatiotemporal Representation of the Face for Automatic Pain Intensity Estimation
Tavakolian, M., & Hadid, A. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8545324 · Cited by 5
Wang, C., Olugbade, T., Mathur, A., Williams, A. C. D. C., Lane, N. D., & Bianchi‐Berthouze, N. (2019) · arXiv (Cornell University) · Cited by 7
Alhamdoosh, F., Pala, P., & Berretti, S. (2025) · IEEE Transactions on Biometrics Behavior and Identity Science · doi:10.1109/tbiom.2025.3592836 · Cited by 4
Predicting vasovagal reactions to needles from video data using 2D-CNN with GRU and LSTM
Rudokaite, J., Ong, L. S., Ertuğrul, I. Ö., Janssen, M. P., & Veld, E. M. J. H. I. ’. (2025) · PLoS ONE · doi:10.1371/journal.pone.0314038 · Cited by 6
Attention-driven contextual feature fusion network for facial videos-based depression recognition
Xiao, H., Liu, X., Zhou, T., Qian, L., & Huang, X. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108806 · Cited by 4
Semwal, A., & Londhe, N. D. (2021) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc51732.2021.9376052 · Cited by 4
AI-Based Visual Early Warning System
Al-Tekreeti, Z. S. I., Moreno-Cuesta, J., Madrigal-Garcia, M. I., & Rodrigues, M. (2024) · Informatics · doi:10.3390/informatics11030059 · Cited by 4
Sign Language-to-Emotion-Specific Text Translation: A Review
Mannepula, S., Hrishitha, M., Reddy, R. R., & Ramu, S. C. (2024) · International Conference on Automation, Computing and Renewable Systems (ICACRS) · doi:10.1109/icacrs62842.2024.10841673 · Cited by 4
Metadata
- Authors
- Rodriguez, P., Cucurull, G., Gonzalez, J., Gonfaus, J. M., Nasrollahi, K., Moeslund, T. B., & Roca, F. X.
- Affiliations
- Computer Vision Center, Universitat Autònoma de Barcelona, Barcelona, Spain
- Universitat Autònoma de Barcelona (ES)
- Aalborg University (DK)
- Venue
- IEEE Transactions on Cybernetics
- Published
- 2022-05
- Volume
- 52
- Issue
- 5
- Pages
- 3314-3324
- ISSN
- 2168-2267 (ISSN-L)
- 2168-2275
- OpenAlex
- W2587128043
- Cited by
- 297
- References
- 69
- Citations by year
- 2026: 12 · 2025: 28 · 2024: 33 · 2023: 32 · 2022: 26
- FWCI
- 43.48
- Type
- journal article
- Language
- en
- Open access
- green
- vbn.aau.dk
- Licence
- Locations
- IEEE Transactions on Cybernetics (publishedVersion)
- PubMed (publishedVersion)
- VBN Forskningsportal (Aalborg Universitet) (acceptedVersion, other-oa)
- Dipòsit Digital de Documents de la UAB (Universitat Autònoma de Barcelona) (submittedVersion, public-domain)
- Funders
- Spanish Project (TIN2015-65464-R)
- Generalitat de Catalunya (2016FI_B 01163)
- COST Action IC1307 iV&L Net (European Network on Integrating Vision and Language) through COST
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- 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
- Keywords
- pain assessment, facial expression recognition, long short-term memory networks, deep pain, video sequences, temporal modeling, class imbalance
- MeSH terms
- Emotions, Facial Expression, Humans, Memory, Short-Term, Pain, Neural Networks, Computer
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review