Continuous Pain Intensity Estimation from 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

Facial action coding system

Ekman, P., & Friesen, W. V. (2019) · PsycTESTS Dataset · doi:10.1037/t27734-000 · Cited by 6,019

Active appearance models

Cootes, T. F., Edwards, G. J., & Taylor, C. J. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.927467 · Cited by 5,409

Discrete Cosine Transform

Ahmed, N., Natarajan, T., & Rao, K. R. (1974) · IEEE Transactions on Computers · doi:10.1109/t-c.1974.223784 · Cited by 5,156

The JPEG still picture compression standard

Wallace, G. K. (1991) · Communications of the ACM · doi:10.1145/103085.103089 · Cited by 3,804

The Behavioral and Brain Sciences

D., C. P. (1979) · The American Journal of Psychology · doi:10.2307/1421932 · Cited by 2,946

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

Painful data: The UNBC-McMaster shoulder pain expression archive database

Lucey, P., Cohn, J. F., Prkachin, K. M., Solomon, P. E., & Matthews, I. (2011) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2011.5771462 · Cited by 634

Facial expression of pain: An evolutionary account

Williams, A. C. D. C. (2002) · Behavioral and Brain Sciences · doi:10.1017/s0140525x02000080 · Cited by 612

The structure, reliability and validity of pain expression: Evidence from patients with shoulder pain

Prkachin, K. M., & Solomon, P. E. (2008) · Pain · doi:10.1016/j.pain.2008.04.010 · Cited by 472

Computer Mediated Automatic Detection of Pain-Related Behavior: Prospect, Progress, Perils

Prkachin, K. M., & Hammal, Z. (2021) · Frontiers in Pain Research · doi:10.3389/fpain.2021.788606 · Cited by 21

Complex Emotion Profiling: An Incremental Active Learning Based Approach With Sparse Annotations

Thuseethan, S., Rajasegarar, S., & Yearwood, J. L. (2020) · IEEE Access · doi:10.1109/access.2020.3015917 · Cited by 21

2D Facial Expression and Movement of Motion for Pain Identification With Deep Learning Methods

Pikulkaew, K., Boonchieng, W., Boonchieng, E., & Chouvatut, V. (2021) · IEEE Access · doi:10.1109/access.2021.3101396 · Cited by 21

Heat-Map Based Emotion and Face Recognition from Thermal Images

Ilikci, B., Chen, L., Cho, H., & Liu, Q. (2019) · Computing, Communications and IoT Applications (ComComAp) · doi:10.1109/comcomap46287.2019.9018786 · 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

Facial Image-Based Automatic Assessment of Equine Pain

Pessanha, F., Salah, A. A., van Loon, T., & Veltkamp, R. C. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3177639 · Cited by 19

Facial Expression Analysis for Estimating Pain in Clinical Settings

Sikka, K. (2014) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2663204.2666282 · Cited by 17

Deep graph neural network for video-based facial pain expression assessment

Patania, S., Boccignone, G., Buršić, S., D'Amelio, A., & Lanzarotti, R. (2022) · ACM Symposium on Applied Computing · doi:10.1145/3477314.3507094 · Cited by 21

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

Audio-Visual Recognition of Pain Intensity

Thiam, P., Kessler, V., Walter, S., Palm, G., & Schwenker, F. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59259-6_10 · Cited by 18

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

Alone versus In-a-group

Mou, W., Güneş, H., & Patras, I. (2019) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3321509 · Cited by 20

FERCE: Facial Expression Recognition for Combined Emotions Using FERCE Algorithm

Swaminathan, A., Vadivel, A., & Arock, M. (2020) · IETE Journal of Research · doi:10.1080/03772063.2020.1756471 · 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

COMPARATIVE LEARNING APPLIED TO INTENSITY RATING OF FACIAL EXPRESSIONS OF PAIN

Werner, P., Al-Hamadi, A. K., & Niese, R. (2014) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001414510082 · Cited by 20

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

Pain recognition and pain empathy from a human-centered AI perspective

Cao, S., Fu, D., Xu, Y., Wermter, S., Liu, X., & Wu, H. (2024) · iScience · doi:10.1016/j.isci.2024.110570 · Cited by 28

Automatic, Objective, and Efficient Measurement of Pain Using Automated Face Analysis

Hammal, Z., & Cohn, J. F. (2018) · Springer eBooks · doi:10.1007/978-3-319-78340-6_7 · Cited by 17

Using Radial Basis Function Neural Networks for Continuous and Discrete Pain Estimation from Bio-physiological Signals

Amirian, M., Kächele, M., & Schwenker, F. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46182-3_23 · Cited by 17

An efficient approach for facial action unit intensity detection using distance metric learning based on cosine similarity

Rathee, N., & Ganotra, D. (2018) · Signal Image and Video Processing · doi:10.1007/s11760-018-1255-3 · 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

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

Real-time facial action unit intensity prediction with regularized metric learning

Nicolle, J., Bailly, K., & Chétouani, M. (2016) · Image and Vision Computing · doi:10.1016/j.imavis.2016.03.004 · Cited by 16

Feature and label relation modeling for multiple-facial action unit classification and intensity estimation

Wang, S., Yang, J., Gao, Z., & Ji, Q. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.12.007 · Cited by 16

Calibration of Vision-Based Measurement of Pain Intensity With Multiple Expert Observers

Casti, P., Mencattini, A., Comes, M. C., Callari, G., Giuseppe, D. D., Natoli, S., Dauri, M., Daprati, E., & Martinelli, E. (2019) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2909603 · Cited by 16

Machine-based Multimodal Pain Assessment Tool for Infants: A ReviewPreprint

Zamzmi, G., Goldgof, D. B., Kasturi, R., Sun, Y., & Ashmeade, T. L. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.00331 · Cited by 23

Explainable Early Stopping for Action Unit Recognition

Corneanu, C. A., Madadi, M., Escalera, S., & Martı́nez, A. M. (2020) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg47880.2020.00080 · Cited by 15

A new framework with multiple tasks for detecting and locating pain events in video

Chen, J., Chi, Z., & Fu, H. C. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.11.003 · 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

Pain intensity estimation by a self-taught selection of histograms of topographical features

Florea, C., Florea, L., Butnaru, R., Bandrabur, A., & Vertan, C. (2016) · Image and Vision Computing · doi:10.1016/j.imavis.2016.08.014 · Cited by 14

An Emerging Decision Authority

Töniges, T., Ötting, S. K., Wrede, B., Maier, G. W., & Sagerer, G. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-803801-7.00026-2 · Cited by 15

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

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

Deep Hybrid Spatiotemporal Networks for Continuous Pain Intensity Estimation

Thuseethan, S., Rajasegarar, S., & Yearwood, J. L. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-36718-3_38 · 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

Global-local combined features to detect pain intensity from facial expression images with attention mechanism

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

Machine Learning Methods for Social Signal Processing

Rudovic, O. O., Nicolaou, M. A., & Pavlović, V. (2017) · Cambridge University Press eBooks · doi:10.1017/9781316676202.018 · Cited by 13

Pain Detection Using Deep Learning with Evaluation System

Pikulkaew, K., Boonchieng, E., Boonchieng, W., & Chouvatut, V. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-5859-7_42 · Cited by 13

Discriminatively Trained Latent Ordinal Model for Video Classification

Sikka, K., & Sharma, G. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2741482 · Cited by 12

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

Eleftheriadis, S., Rudovic, O. O., Deisenroth, M. P., & Pantić, M. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-54184-6_10 · Cited by 12

Cumulative attributes for pain intensity estimation

Egede, J., & Valstar, M. (2017) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3136755.3136789 · Cited by 12

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

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

Video-based discomfort detection for infants using a Constrained Local Model

Li, C., Zinger, S., Ten, W. E. T. A., & de With, P. H. N. (2016) · International Conference on Systems, Signals and Image Processing · doi:10.1109/iwssip.2016.7502709 · Cited by 11

Continuous Multimodal Human Affect Estimation using Echo State Networks

Amirian, M., Kächele, M., Thiam, P., Kessler, V., & Schwenker, F. (2016) · International Workshop on Audio/Visual Emotion Challenge (AVEC) · doi:10.1145/2988257.2988260 · Cited by 11

Dynamic Behavior Analysis via Structured Rank Minimization

Georgakis, C., Panagakis, Y., & Pantić, M. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-016-0985-3 · 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

Pain Expression Recognition Using Occluded Faces

Ashraf, A. B., Yang, A., & Taati, B. (2019) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2019.8756594 · 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

ECCNet: An Ensemble of Compact Convolution Neural Network for Pain Severity Assessment from Face images

Semwal, A., & Londhe, N. D. (2021) · International Conference on Cloud Computing, Data Science & Engineering (Confluence) · doi:10.1109/confluence51648.2021.9377197 · Cited by 11

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

Positive/Negative Emotion Detection from RGB-D Upper Body Images

Ballihi, L., Lablack, A., Amor, B. B., Bilasco, I. M., & Daoudi, M. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-13737-7_10 · Cited by 10

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

Towards Multimodal Pain Assessment for Research and Clinical Use

Hammal, Z., & Cohn, J. F. (2014) · doi:10.1145/2666253.2666257 · Cited by 10

Context-Sensitive Conditional Ordinal Random Fields for Facial Action Intensity Estimation

Rudovic, O. O., Pavlović, V., & Pantić, M. (2013) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2013.70 · Cited by 10

PCANet-II: When PCANet Meets the Second Order Pooling

Fan, C., Hong, X., Lei, T. I. A. N., Ming, Y., Pietikäinen, M., & Zhao, G. (2018) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2017edl8258 · Cited by 10

Action unit intensity estimation using hierarchical partial least squares

Gehrig, T., Al-Halah, Z., Ekenel, H. K., & Stiefelhagen, R. (2015) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2015.7163152 · 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

Measuring Students' Thermal Comfort and Its Impact on Learning.

Jiang, H., Iandoli, M., Dessel, S. V., Liu, S., & Whitehill, J. (2019) · Educational Data Mining · Cited by 9

Automatic Estimation of Action Unit Intensities and Inference of Emotional Appraisals

Seuß, D., Hassan, T., Dieckmann, A., Unfried, M., Scherer, K. R., Mortillaro, M., & Garbas, J.-U. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3077590 · Cited by 11

Automatic Detection and Intensity Estimation of Spontaneous Smiles

Girard, J. M. (2014) · D-Scholarship@Pitt (University of Pittsburgh) · Cited by 9

General-to-specific learning for facial attribute classification in the wild

Sun, Y., & Yu, J. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2018.09.003 · Cited by 8

Evolutionary Algorithms for the Design of Neural Network Classifiers for the Classification of Pain Intensity

Mamontov, D., Polonskaia, I. S., Skorokhod, A., Semenkin, E. S., Kessler, V., & Schwenker, F. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20984-1_8 · Cited by 8

Methods for facial expression recognition with applications in challenging situations

Huang, X. (2014) · Cited by 7

Spontaneous Expression Detection from 3D Dynamic Sequences by Analyzing Trajectories on Grassmann Manifolds

Alashkar, T., Amor, B. B., Daoudi, M., & Berretti, S. (2016) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2016.2623718 · Cited by 7

Emotion detection based on infrared thermography: A review of machine learning and deep learning algorithms

Calderon‐Uribe, S., Morales-Hernandez, L. A., Guzmán-Sandoval, V. M., Trejo, B. D., & Cruz-Albarrán, I. A. (2024) · Infrared Physics & Technology · doi:10.1016/j.infrared.2024.105669 · Cited by 10

DeepFN: Towards Generalizable Facial Action Unit Recognition with Deep Face Normalization

Hernandez, J., McDuff, D., Rudovic, O. O., Fung, A., & Czerwinski, M. P. (2022) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii55700.2022.9953868 · Cited by 8

Automated 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

Facial Actions as Social Signals

Valstar, M., Zafeiriou, S. P., & Pantić, M. (2017) · Cambridge University Press eBooks · doi:10.1017/9781316676202.011 · Cited by 7

A Scalable Approach for Facial Action Unit Classifier Training UsingNoisy Data for Pre-TrainingPreprint

Fung, A., & McDuff, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.05946 · Cited by 7

Ordinal information based facial expression intensity estimation for emotional interaction: a novel semi-supervised deep learning approach

Xu, R.-Y., Han, J., & Chen, J.-Y. (2022) · Computing · doi:10.1007/s00607-022-01140-y · Cited by 6

Body Tracking in Healthcare

O'Hara, K. P., Morrison, C. P., Sellen, A. J., Bianchi‐Berthouze, N., & Craig, C. (2016) · Synthesis lectures on assistive, rehabilitative, and health-preserving technologies · doi:10.2200/s00702ed1v01y201602arh009 · Cited by 6

Body Tracking in Healthcare

O'Hara, K. P., Morrison, C. P., Sellen, A. J., Bianchi‐Berthouze, N., & Craig, C. (2016) · Synthesis lectures on assistive, rehabilitative, and health-preserving technologies · doi:10.1007/978-3-031-01600-4 · Cited by 6

Patients with limited communication in end-of-life situations: Initial psychometric properties of a discomfort observation scale

Soto-Rubio, A. L., Tomás, J. M., Pérez‐Marín, M., & Martín, P. B. (2017) · Journal of Health Psychology · doi:10.1177/1359105317696139 · Cited by 6

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

Automated Assessment of Pain: Prospects, Progress, and a Path Forward

Prkachin, K. M., & Hammal, Z. (2021) · Companion Publication of the International Conference on Multimodal Interaction (ICMI Companion) · doi:10.1145/3461615.3485671 · Cited by 6

Automated Facial Pain Assessment Using Dual-Attention CNN with Clinically Calibrated High-Reliability and Reproducibility Framework

Sankoh, A. P., Raza, A., Parwez, K., Shishah, W., Alharbi, A. A., Javed, M., & Bilal, M. (2026) · Biomimetics · doi:10.3390/biomimetics11010051 · Cited by 5

Enhanced residual attention-based subject-specific network (ErAS-Net): facial expression-based pain classification with multiple attention mechanisms

Morsali, M., & Ghaffari, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-04552-w · Cited by 5

A Compute-Efficient Algorithm for Robust Eyebrow Detection

Sathyanarayana, S., Satzoda, R. K., Sathyanarayana, S., & Thambipillai, S. (2014) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2014.101 · 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

Clinical Valid Pain Database with Biomarker and Visual Information for Pain Level Analysis

Liu, P., Yazgan, İ., Olsen, S. T., Moser, A., Çiftçi, U. A., Bajwa, S., Tvetenstrand, C. D., Gerhardstein, P. C., Sadik, O. A., & Yin, L. (2018) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2018.00084 · Cited by 5

Analyzing facial action units in children to differentiate genuine and fake pain during inferior alveolar nerve block: a cross-sectional study

Alkhouli, M., Al‐Nerabieah, Z., & Dashash, M. (2023) · Scientific Reports · doi:10.1038/s41598-023-42982-6 · Cited by 5

A new approach for pain event detection in video

Chen, J., Chi, Z., & Fu, H. C. (2015) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2015.7344579 · Cited by 5

Continuous pain related behavior recognition from muscle activity and body movements

Qin, R., Meng, H., & Li, M. (2016) · International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) · doi:10.1109/fskd.2016.7603435 · 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

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

Pain Level Estimation From Videos by Analyzing the Dynamics of Facial Landmarks With a Spatio-Temporal Graph Neural Network

Alhamdoosh, F., Pala, P., & Berretti, S. (2025) · IEEE Transactions on Biometrics Behavior and Identity Science · doi:10.1109/tbiom.2025.3592836 · Cited by 4

Novel Computational Intelligence Techniques for Automatic Pain Detection and Pain Intensity Level Estimation From Facial Expressions Using Distributed Computing for Big Data

Alphonse, A. S., & Dharma, D. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-813314-9.00004-9 · Cited by 4

S-PANET: A Shallow Convolutional Neural Network for Pain Severity Assessment in Uncontrolled Environment

Semwal, A., & Londhe, N. D. (2021) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc51732.2021.9376052 · Cited by 4

Automatic Pain Detection Through Facial Expression

Kumar, V., Dhapola, P., & Kushwaha, A. N. (2023) · Advances in systems analysis, software engineering, and high performance computing book series · doi:10.4018/978-1-6684-8602-3.ch006 · Cited by 4

A Novel Approach for Continuous Pain Intensity Estimation

Rathee, N., & Ganotra, D. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-981-10-1708-7_50 · 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

Von der Fremdbeurteilung des Schmerzes zur automatisierten multimodalen Messung der Schmerzintensität

Frisch, S., Werner, P., Al-Hamadi, A. K., Traue, H. C., Gruss, S., & Walter, S. (2020) · Der Schmerz · doi:10.1007/s00482-020-00473-x · Cited by 4

Emotion detection through fusion of complementary facial features

Rathee, N., Vaish, A., & Gupta, S. (2017) · International Conference on Communication Systems and Network Technologies · doi:10.1109/csnt.2017.8418530 · Cited by 4

Facial Analysis Method for Pain Detection

Subea, O., & Suciu, G. D. (2019) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-23976-3_17 · Cited by 3

Automatic Pain Assessment with Facial Activity Descriptors

Werner, P., Al-Hamadi, A. K., Limbrecht-Ecklundt, K., Walter, S., Gruss, S., & Traue, H. C. (2016) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2016.2537327 · Cited by 166

A Novel Approach for Continuous Pain Intensity Estimation

Rathee, N., & Ganotra, D. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-981-10-1708-7_50 · Cited by 4

Automatic detection of pain intensity

Hammal, Z., & Cohn, J. F. (2012) · Proceedings of the 14th ACM international conference on Multimodal interaction · doi:10.1145/2388676.2388688 · Cited by 185

Learning Appearance Features for Pain Detection Using the UNBC-McMaster Shoulder Pain Expression Archive Database

Pedersen, H. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-20904-3_12 · Cited by 31