22.3.6.1.8 Face Expression Recognition for Pain

Chapter Contents (Back)
Faces, Expression. Facial Expressions. Expressions. Pain.

Hammal, Z.[Zakia], Kunz, M.[Miriam],
Pain monitoring: A dynamic and context-sensitive system,
PR(45), No. 4, April 2012, pp. 1265-1280.
Elsevier DOI 1112
Pain expression; Spontaneous facial expressions; Context based recognition; Transferable Belief Model; Classification BibRef

Hammal, Z.[Zakia],
Efficient Detection of Consecutive Facial Expression Apices Using Biologically Based Log-Normal Filters,
ISVC11(I: 586-595).
Springer DOI 1109
BibRef

Hammal, Z.[Zakia], Arguin, M.[Martin], Gosselin, F.[Frédéric],
Comparing a Transferable Belief Model Capable of Recognizing Facial Expressions with the Latest Human Data,
ISVC07(I: 509-520).
Springer DOI 0711
BibRef

Hammal, Z., Couvreur, L., Caplier, A., Rombaut, M.,
Facial Expression Recognition Based on the Belief Theory: Comparison with Different Classifiers,
CIAP05(743-752).
Springer DOI 0509
BibRef

Littlewort, G.C.[Gwen C.], Bartlett, M.S.[Marian Stewart], Lee, K.[Kang],
Automatic coding of facial expressions displayed during posed and genuine pain,
IVC(27), No. 12, November 2009, pp. 1797-1803.
Elsevier DOI 0910
Machine learning; Malingering; Facial expression; Spontaneous behavior; Automated FACS BibRef

Ashraf, A.B.[Ahmed Bilal], Lucey, S.[Simon], Cohn, J.F.[Jeffrey F.], Chen, T.H.[Tsu-Han], Ambadar, Z.[Zara], Prkachin, K.M.[Kenneth M.], Solomon, P.E.[Patricia E.],
The painful face: Pain expression recognition using active appearance models,
IVC(27), No. 12, November 2009, pp. 1788-1796.
Elsevier DOI 0910
Active appearance models; Support vector machines; Pain; Facial expression; Automatic facial image analysis; FACS BibRef

Lucey, P.[Patrick], Cohn, J.F.[Jeffrey F.], Prkachin, K.M.[Kenneth M.], Solomon, P.E.[Patricia E.], Chew, S.[Sien], Matthews, I.[Iain],
Painful monitoring: Automatic pain monitoring using the UNBC-McMaster shoulder pain expression archive database,
IVC(30), No. 3, March 2012, pp. 197-205.
Elsevier DOI 1204
Pain; Active Appearance Models (AAMs); Action Units (AUs); FACS BibRef

Lucey, P.[Patrick], Cohn, J.F.[Jeffrey F.], Prkachin, K.M.[Kenneth M.], Solomon, P.E.[Patricia E.], Matthews, I.[Iain],
Painful data: The UNBC-McMaster shoulder pain expression archive database,
FG11(57-64).
IEEE DOI 1103
Dataset, Facial Expression. BibRef

Lucey, P.[Patrick], Cohn, J.F.[Jeffrey F.], Matthews, I., Lucey, S.[Simon], Sridharan, S.[Sridha], Howlett, J., Prkachin, K.M.[Kenneth M.],
Automatically Detecting Pain in Video Through Facial Action Units,
SMC-B(41), No. 3, June 2011, pp. 664-674.
IEEE DOI 1106
BibRef

Lucey, P.[Patrick], Cohn, J.F.[Jeffrey F.], Lucey, S.[Simon], Sridharan, S.[Sridha], Prkachin, K.M.[Kenneth M.],
Automatically detecting action units from faces of pain: Comparing shape and appearance features,
CVPR4HB09(12-18).
IEEE DOI 0906
BibRef

Sikka, K.[Karan], Dhall, A.[Abhinav], Bartlett, M.S.[Marian Stewart],
Classification and weakly supervised pain localization using multiple segment representation,
IVC(32), No. 10, 2014, pp. 659-670.
Elsevier DOI 1410
BibRef
Earlier:
Weakly supervised pain localization using multiple instance learning,
FG13(1-8)
IEEE DOI 1309
Emotion classification. face recognition BibRef

Sikka, K.[Karan], Wu, T.F.[Ting-Fan], Susskind, J.[Josh], Bartlett, M.S.[Marian Stewart],
Exploring Bag of Words Architectures in the Facial Expression Domain,
Face12(II: 250-259).
Springer DOI 1210
BibRef

Deriso, D.M.[David M.], Susskind, J.[Josh], Tanaka, J.[Jim], Winkielman, P.[Piotr], Herrington, J.[John], Schultz, R.[Robert], Bartlett, M.S.[Marian Stewart],
Exploring the Facial Expression Perception-Production Link Using Real-Time Automated Facial Expression Recognition,
Face12(II: 270-279).
Springer DOI 1210
BibRef

Rathee, N.[Neeru], Ganotra, D.[Dinesh],
A novel approach for pain intensity detection based on facial feature deformations,
JVCIR(33), No. 1, 2015, pp. 247-254.
Elsevier DOI 1512
Thin Plate Spline BibRef

Rathee, N.[Neeru], Ganotra, D.[Dinesh],
An efficient approach for facial action unit intensity detection using distance metric learning based on cosine similarity,
SIViP(12), No. 6, September 2018, pp. 1141-1148.
WWW Link. 1808
BibRef

Rathee, N.[Neeru], Ganotra, D.[Dinesh],
Multiview Distance Metric Learning on facial feature descriptors for automatic pain intensity detection,
CVIU(147), No. 1, 2016, pp. 77-86.
Elsevier DOI 1605
Multiview Distance Metric Learning BibRef

Kaltwang, S.[Sebastian], Todorovic, S., Pantic, M.[Maja],
Doubly Sparse Relevance Vector Machine for Continuous Facial Behavior Estimation,
PAMI(38), No. 9, September 2016, pp. 1748-1761.
IEEE DOI 1609
emotion recognition BibRef

Kaltwang, S.[Sebastian], Rudovic, O.[Ognjen], Pantic, M.[Maja],
Continuous Pain Intensity Estimation from Facial Expressions,
ISVC12(II: 368-377).
Springer DOI 1209
BibRef

Martinez, D.L., Rudovic, O.[Ognjen], Picard, R.W.[Rosalind W.],
Personalized Automatic Estimation of Self-Reported Pain Intensity from Facial Expressions,
DeepAffective17(2318-2327)
IEEE DOI 1709
Estimation, Face, Hidden Markov models, Image sequences, Pain, Recurrent neural networks, Reliability BibRef

Florea, C.[Corneliu], Florea, L.[Laura], Butnaru, R.[Raluca], Bandrabur, A.[Alessandra], Vertan, C.[Constantin],
Pain intensity estimation by a self-taught selection of histograms of topographical features,
IVC(56), No. 1, 2016, pp. 13-27.
Elsevier DOI 1609
Histograms of Topographical (HoT) features BibRef

Aung, M.S.H., Kaltwang, S., Romera-Paredes, B., Martinez, B., Singh, A., Cella, M., Valstar, M., Meng, H., Kemp, A., Shafizadeh, M., Elkins, A.C., Kanakam, N., de Rothschild, A., Tyler, N., Watson, P.J., de C. Williams, A.C.[Amanda C.], Pantic, M., Bianchi-Berthouze, N.,
The Automatic Detection of Chronic Pain-Related Expression: Requirements, Challenges and the Multimodal EmoPain Dataset,
AffCom(7), No. 4, October 2016, pp. 435-451.
IEEE DOI 1612
Context modeling BibRef

Olugbade, T.A.[Temitayo A.], Bianchi-Berthouze, N.[Nadia], Marquardt, N.[Nicolai], de C. Williams, A.C.[Amanda C.],
Human Observer and Automatic Assessment of Movement Related Self-Efficacy in Chronic Pain: From Exercise to Functional Activity,
AffCom(11), No. 2, April 2020, pp. 214-229.
IEEE DOI 2006
Pain, Observers, Muscles, Wearable sensors, Psychology, Affective computing, bodily expressions, bodily muscle activity, self-efficacy BibRef

Lo Presti, L.[Liliana], La Cascia, M.[Marco],
Boosting Hankel matrices for face emotion recognition and pain detection,
CVIU(156), No. 1, 2017, pp. 19-33.
Elsevier DOI 1702
BibRef
Earlier:
Using Hankel matrices for dynamics-based facial emotion recognition and pain detection,
AMFG15(26-33)
IEEE DOI 1510
BibRef
And:
Ensemble of Hankel Matrices for Face Emotion Recognition,
CIAP15(II:586-597).
Springer DOI 1511
Emotion recognition BibRef

Werner, P.[Philipp], Al-Hamadi, A.[Ayoub], Limbrecht-Ecklundt, K., Walter, S.[Steffen], Gruss, S.[Sascha], Traue, H.C.[Harald C.],
Automatic Pain Assessment with Facial Activity Descriptors,
AffCom(8), No. 3, July 2017, pp. 286-299.
IEEE DOI 1709
Databases, Face recognition, Feature extraction, Heating, Observers, Pain, Reliability, Automatic pain assessment, facial dynamics, facial expression analysis, health care, pain intensity, recognition BibRef

Pandya, N.[Nikul], Werner, P.[Philipp], Al-Hamadi, A.[Ayoub],
Deep Facial Expression Recognition with Occlusion Regularization,
ISVC20(II:410-420).
Springer DOI 2103
BibRef

Ruiz, A.[Adria], Rudovic, O.[Ognjen], Binefa, X.[Xavier], Pantic, M.[Maja],
Multi-Instance Dynamic Ordinal Random Fields for Weakly Supervised Facial Behavior Analysis,
IP(27), No. 8, August 2018, pp. 3969-3982.
IEEE DOI 1806
BibRef
Earlier:
Multi-Instance Dynamic Ordinal Random Fields for Weakly-Supervised Pain Intensity Estimation,
ACCV16(II: 171-186).
Springer DOI 1704
graph theory, image processing, random processes, regression analysis, unsupervised learning, undirected graphical models BibRef

Ruiz, A.[Adria], van de Weijer, J.[Joost], Binefa, X.[Xavier],
From Emotions to Action Units with Hidden and Semi-Hidden-Task Learning,
ICCV15(3703-3711)
IEEE DOI 1602
BibRef
Earlier:
Regularized Multi-Concept MIL for weakly-supervised facial behavior categorization,
BMVC14(xx-yy).
HTML Version. 1410
BibRef

Wang, J.W.[Jin-Wei], Sun, H.Z.[Hua-Zhi],
Pain Intensity Estimation Using Deep Spatiotemporal and Handcrafted Features,
IEICE(E101-D), No. 6, June 2018, pp. 1572-1580.
WWW Link. 1806
BibRef

Zhi, R.C.[Rui-Cong], Zamzmi, G.[Ghada], Goldgof, D.[Dmitry], Ashmeade, T.[Terri], Li, T.T.[Ting-Ting], Sun, Y.[Yu],
Infants' Pain Recognition Based on Facial Expression: Dynamic Hybrid Descriptions,
IEICE(E101-D), No. 7, July 2018, pp. 1860-1869.
WWW Link. 1807
BibRef

Virrey, R.A.[Reneiro Andal], Liyanage, C.D.[Chandratilak De_Silva], bin Pg Hj Petra, M.I.[Mohammad Iskandar], Abas, P.E.[Pg Emeroylariffion],
Visual data of facial expressions for automatic pain detection,
JVCIR(61), 2019, pp. 209-217.
Elsevier DOI 1906
Facial expression recognition, Emotion database, Human pain detection, Feature learning BibRef

Sun, Y.[Yue], Shan, C.F.[Cai-Feng], Tan, T.[Tao], Long, X.[Xi], Pourtaherian, A.[Arash], Zinger, S.[Svitlana], de With, P.H.N.[Peter H. N.],
Video-based discomfort detection for infants,
MVA(30), No. 5, July 2019, pp. 933-944.
Springer DOI 1907
BibRef

Tavakolian, M.[Mohammad], Hadid, A.[Abdenour],
A Spatiotemporal Convolutional Neural Network for Automatic Pain Intensity Estimation from Facial Dynamics,
IJCV(127), No. 10, October 2019, pp. 1413-1425.
Springer DOI 1909
BibRef
Earlier:
Deep Spatiotemporal Representation of the Face for Automatic Pain Intensity Estimation,
ICPR18(350-354)
IEEE DOI 1812
BibRef
And:
Deep Binary Representation of Facial Expressions: A Novel Framework for Automatic Pain Intensity Recognition,
ICIP18(1952-1956)
IEEE DOI 1809
Pain, Kernel, Convolution, Binary codes, Databases, Face, Feature extraction, Estimation, Spatiotemporal phenomena. Hamming distance, Estimation, Pain Assessment, Clinical Diagnosis BibRef

Peng, X.L.[Xian-Lin], Huang, D.[Dong], Zhang, H.X.[Hai-Xi],
Pain intensity recognition via multi-scale deep network,
IET-IPR(14), No. 8, 19 June 2020, pp. 1645-1652.
DOI Link 2005
BibRef

Rivas, J.J.[Jesús Joel], Orihuela-Espina, F.[Felipe], Palafox, L.[Lorena], Bianchi-Berthouze, N.[Nadia], del Carmen Lara, M.[María], Hernández-Franco, J.[Jorge], Sucar, L.E.[Luis Enrique],
Unobtrusive Inference of Affective States in Virtual Rehabilitation from Upper Limb Motions: A Feasibility Study,
AffCom(11), No. 3, July 2020, pp. 470-481.
IEEE DOI 2008
Pain, Medical treatment, Games, Bayes methods, Support vector machines, Computer science, Fatigue, semi-Naďve Bayesian classifier BibRef

Tavakolian, M.[Mohammad], Lopez, M.B.[Miguel Bordallo], Liu, L.[Li],
Self-supervised pain intensity estimation from facial videos via statistical spatiotemporal distillation,
PRL(140), 2020, pp. 26-33.
Elsevier DOI 2012
Self-supervised learning, Representation learning, Pain assessment, Statistical spatiotemporal distillation BibRef

Nezam, T., Boostani, R., Abootalebi, V., Rastegar, K.,
A Novel Classification Strategy to Distinguish Five Levels of Pain Using the EEG Signal Features,
AffCom(12), No. 1, January 2021, pp. 131-140.
IEEE DOI 2103
Pain, Electroencephalography, Feature extraction, Electrooculography, Electromyography, Correlation, Muscles, and decision tree BibRef

Kharghanian, R.[Reza], Peiravi, A.[Ali], Moradi, F.[Farshad], Iosifidis, A.[Alexandros],
Pain detection using batch normalized discriminant restricted Boltzmann machine layers,
JVCIR(76), 2021, pp. 103062.
Elsevier DOI 2104
Pain detection, Convolutional deep belief network, Discriminant Feature Learning, Representation learning, Batch Normalization BibRef

Hassan, T.[Teena], Seuß, D.[Dominik], Wollenberg, J.[Johannes], Weitz, K.[Katharina], Kunz, M.[Miriam], Lautenbacher, S.[Stefan], Garbas, J.U.[Jens-Uwe], Schmid, U.[Ute],
Automatic Detection of Pain from Facial Expressions: A Survey,
PAMI(43), No. 6, June 2021, pp. 1815-1831.
IEEE DOI 2106
Survey, Pain. Pain, Feature extraction, Task analysis, Imaging, Encoding, Observers, Machine learning, Automatic pain detection, survey BibRef

Rajasekhar, G.P.[Gnana Praveen], Granger, E.[Eric], Cardinal, P.[Patrick],
Deep domain adaptation with ordinal regression for pain assessment using weakly-labeled videos,
IVC(110), 2021, pp. 104167.
Elsevier DOI 2106
BibRef
Earlier:
Deep Weakly Supervised Domain Adaptation for Pain Localization in Videos,
FG20(473-480)
IEEE DOI 2102
Deep domain adaptation, Weakly-supervised learning, Multiple instance learning, Ordinal regression, Pain intensity estimation. Pain, Videos, Adaptation models, Estimation, Location awareness, Hidden Markov models, Biological system modeling, Facial Expressions. BibRef

Thiam, P.[Patrick], Kessler, V.[Viktor], Amirian, M.[Mohammadreza], Bellmann, P.[Peter], Layher, G.[Georg], Zhang, Y.[Yan], Velana, M.[Maria], Gruss, S.[Sascha], Walter, S.[Steffen], Traue, H.C.[Harald C.], Schork, D.[Daniel], Kim, J.H.[Jong-Hwa], André, E.[Elisabeth], Neumann, H.[Heiko], Schwenker, F.[Friedhelm],
Multi-Modal Pain Intensity Recognition Based on the SenseEmotion Database,
AffCom(12), No. 3, July 2021, pp. 743-760.
IEEE DOI 2109
Pain, Databases, Physiology, Computer architecture, Electromyography, Feature extraction, Reliability, Pain intensity recognition, signal processing BibRef

Xin, X.[Xuwu], Li, X.W.[Xiao-Wu], Yang, S.F.[Sheng-Fu], Lin, X.Y.[Xiao-Yan], Zheng, X.[Xin],
Pain expression assessment based on a locality and identity aware network,
IET-IPR(15), No. 12, 2021, pp. 2948-2958.
DOI Link 2109
BibRef

Zhi, R.C.[Rui-Cong], Zhou, C.X.[Cai-Xia], Yu, J.W.[Jun-Wei], Li, T.T.[Ting-Ting], Zamzmi, G.[Ghada],
Multimodal-Based Stream Integrated Neural Networks for Pain Assessment,
IEICE(E104-D), No. 12, December 2021, pp. 2184-2194.
WWW Link. 2112
BibRef

Zhi, R.C.[Rui-Cong], Zhou, C.X.[Cai-Xia], Yu, J.W.[Jun-Wei], Liu, S.[Shuai],
Multi-stream Integrated Neural Networks for Facial Expression-based Pain Recognition,
CAIHA20(28-35).
Springer DOI 2103
BibRef

Romeo, L.[Luca], Cavallo, A.[Andrea], Pepa, L.[Lucia], Bianchi-Berthouze, N.[Nadia], Pontil, M.[Massimiliano],
Multiple Instance Learning for Emotion Recognition Using Physiological Signals,
AffCom(13), No. 1, January 2022, pp. 389-407.
IEEE DOI 2203
Labeling, Pain, Machine learning, Standards, Task analysis, Physiology, Computational modeling, Emotion recognition, diverse density BibRef

Werner, P.[Philipp], Lopez-Martinez, D.[Daniel], Walter, S.[Steffen], Al-Hamadi, A.[Ayoub], Gruss, S.[Sascha], Picard, R.W.[Rosalind W.],
Automatic Recognition Methods Supporting Pain Assessment: A Survey,
AffCom(13), No. 1, January 2022, pp. 530-552.
IEEE DOI 2203
Pain, Gold, Affective computing, Tools, Tissue damage, Nervous system, Physiology, Pain assessment, recognition, survey, review BibRef

Zamzmi, G.[Ghada], Pai, C.Y.[Chih-Yun], Goldgof, D.[Dmitry], Kasturi, R.[Rangachar], Ashmeade, T.[Terri], Sun, Y.[Yu],
A Comprehensive and Context-Sensitive Neonatal Pain Assessment Using Computer Vision,
AffCom(13), No. 1, January 2022, pp. 28-45.
IEEE DOI 2203
Pain, Pediatrics, Feature extraction, Support vector machines, Physiology, Protocols, Principal component analysis, physiological BibRef

Chen, Z.L.[Zhan-Li], Ansari, R.[Rashid], Wilkie, D.J.[Diana J.],
Learning Pain from Action Unit Combinations: A Weakly Supervised Approach via Multiple Instance Learning,
AffCom(13), No. 1, January 2022, pp. 135-146.
IEEE DOI 2203
Pain, Gold, Machine learning, Encoding, Feature extraction, Reliability, Face recognition, FACS, action unit combinations, multiple instance learning BibRef

Rodriguez, P.[Pau], Cucurull, G.[Guillem], Gonzŕlez, J.[Jordi], Gonfaus, J.M.[Josep M.], Nasrollahi, K.[Kamal], Moeslund, T.B.[Thomas B.], Roca, F.X.[F. Xavier],
Deep Pain: Exploiting Long Short-Term Memory Networks for Facial Expression Classification,
Cyber(52), No. 5, May 2022, pp. 3314-3324.
IEEE DOI 2206
Pain, Feature extraction, Hidden Markov models, Face, Estimation, Databases, Face recognition, Affective computing BibRef

Dirupo, G.[Giada], Garlasco, P.[Paolo], Chappuis, C.[Cyrielle], Sharvit, G.[Gil], Corradi-Dell'Acqua, C.[Corrado],
State-Specific and Supraordinal Components of Facial Response to Pain,
AffCom(13), No. 2, April 2022, pp. 793-804.
IEEE DOI 2206
Pain, Gold, Tools, Temperature measurement, Face, Olfactory, Diagnosis or assessment, emotion in human-computer interaction, synthesis of affective behavior BibRef

Huang, D.[Dong], Feng, X.Y.[Xiao-Yi], Zhang, H.X.[Hai-Xi], Yu, Z.T.[Zi-Tong], Peng, J.Y.[Jin-Ye], Zhao, G.Y.[Guo-Ying], Xia, Z.Q.[Zhao-Qiang],
Spatio-Temporal Pain Estimation Network With Measuring Pseudo Heart Rate Gain,
MultMed(24), 2022, pp. 3300-3313.
IEEE DOI 2207
Pain, Estimation, Feature extraction, Visualization, Physiology, Videos, Pain estimation, pseudo modality, spatio-temporal network, probabilistic inference BibRef

Xiang, X.[Xiang], Wang, F.[Feng], Tan, Y.[Yuwen], Yuille, A.L.[Alan L.],
Imbalanced regression for intensity series of pain expression from videos by regularizing spatio-temporal face nets,
PRL(163), 2022, pp. 152-158.
Elsevier DOI 2212
Facial expression, LSTM, Fine-tuning, Regularization BibRef

Wang, F.[Feng], Xiang, X.[Xiang], Liu, C.[Chang], Tran, T.D.[Trac D.], Reiter, A.[Austin], Hager, G.D.[Gregory D.], Quon, H.[Harry], Cheng, J.[Jian], Yuille, A.L.[Alan L.],
Regularizing face verification nets for pain intensity regression,
ICIP17(1087-1091)
IEEE DOI 1803
Biomedical monitoring, Convolution, Distance measurement, Face, Pain, Training, CNN, fine-tuning, regression, regularizer BibRef

Szczapa, B.[Benjamin], Daoudi, M.[Mohamed], Berretti, S.[Stefano], Pala, P.[Pietro], del Bimbo, A.[Alberto], Hammal, Z.[Zakia],
Automatic Estimation of Self-Reported Pain by Trajectory Analysis in the Manifold of Fixed Rank Positive Semi-Definite Matrices,
AffCom(13), No. 4, October 2022, pp. 1813-1826.
IEEE DOI 2212
Pain, Trajectory, Manifolds, Estimation, Videos, Computational modeling, Face recognition, Facial landmarks, trajectory on a manifold BibRef

Yan, J.J.[Jing-Jie], Lu, G.M.[Guan-Ming], Li, X.N.[Xiao-Nan], Zheng, W.M.[Wen-Ming], Huang, C.W.[Cheng-Wei], Cui, Z.[Zhen], Zong, Y.[Yuan], Chen, M.Y.[Meng-Ying], Hao, Q.[Qiang], Liu, Y.[Yi], Zhu, J.[Jindu], Li, H.B.[Hai-Bo],
FENP: A Database of Neonatal Facial Expression for Pain Analysis,
AffCom(14), No. 1, January 2023, pp. 245-254.
IEEE DOI 2303
Databases, Pain, Pediatrics, Face recognition, Biomedical imaging, Encoding, Hospitals, Facial expression recognition, neonatal pain, facial expression database BibRef

Huang, D.[Dong], Xia, Z.Q.[Zhao-Qiang], Li, L.[Lei], Ma, Y.P.[Yu-Peng],
Pain estimation with integrating global-wise and region-wise convolutional networks,
IET-IPR(17), No. 3, 2023, pp. 637-648.
DOI Link 2303
BibRef

Othman, E.[Ehsan], Werner, P.[Philipp], Saxen, F.[Frerk], Al-Hamadi, A.[Ayoub], Gruss, S.[Sascha], Walter, S.[Steffen],
Classification networks for continuous automatic pain intensity monitoring in video using facial expression on the X-ITE Pain Database,
JVCIR(91), 2023, pp. 103743.
Elsevier DOI 2303
Continuous pain intensity recognition, Random Forest classifier, Facial expression, Sample weighting BibRef

Bobby, J.S.[J. Sofia], Kapali, B.S.C.[B. Suresh Chander], Kumar, U.S.[Ushus S.], Femina, M.A.,
QCBO-WSVM: Quantum chaos butterfly optimization-based weighted support vector machine for neuropathic pain detection from EEG signal,
IJIST(33), No. 5, 2023, pp. 1606-1620.
DOI Link 2310
brain-computer Interface, central nervous system injury, central neuropathic pain, common spatial patterns, weighted incremental-decremental support vector machine classifier BibRef

Pessanha, F.[Francisca], Salah, A.A.[Albert Ali], van Loon, T.[Thijs], Veltkamp, R.[Remco],
Facial Image-Based Automatic Assessment of Equine Pain,
AffCom(14), No. 3, July 2023, pp. 2064-2076.
IEEE DOI 2310
BibRef

Anter, A.M.[Ahmed M.], Zhang, Z.G.[Zhi-Guo],
RLWOA-SOFL: A New Learning Model-Based Reinforcement Swarm Intelligence and Self-Organizing Deep Fuzzy Rules for fMRI Pain Decoding,
AffCom(15), No. 2, April 2024, pp. 644-656.
IEEE DOI 2406
Pain, Functional magnetic resonance imaging, Decoding, Whales, Feature extraction, Brain modeling, and pain decoding BibRef

Olugbade, T.[Temitayo], de C Williams, A.C.[Amanda C.], Gold, N.[Nicolas], Bianchi-Berthouze, N.[Nadia],
Movement Representation Learning for Pain Level Classification,
AffCom(15), No. 3, July 2024, pp. 1303-1314.
IEEE DOI 2409
Pain, Representation learning, Task analysis, Data models, Computer architecture, Statistics, Sociology, Activity recognition, transfer learning BibRef


Alves, B.[Bruna], Silva, C.[Catarina], Sebastiăo, R.[Raquel],
Do Emotional States Influence Physiological Pain Responses?,
CIARP23(II:117-131).
Springer DOI 2312
BibRef

Grissette, H.[Hanane], Nfaoui, E.[El_Habib],
Do Patients Tend to Find Positive or Negative Feedback on Social Networks? A Study of The Main Aspects of Modelling Patient Understanding Based on Emotional Variants,
ISCV22(1-8)
IEEE DOI 2208
Representation learning, Training, Ethics, Sentiment analysis, Negative feedback, Social networking (online), Pain, Social networks BibRef

Zarghami, Y., Mafeld, S., Conway, A., Taati, B.,
Pain Detection in Masked Faces during Procedural Sedation,
FG23(1-6)
IEEE DOI 2303
Smoothing methods, Medical devices, Pain, Computational modeling, Receivers, Gesture recognition BibRef

Vu, M.T.[Manh Tu], Beurton-Aimar, M.[Marie],
Learning to focus on region-of-interests for pain intensity estimation,
FG23(1-6)
IEEE DOI 2303
Training, Deep learning, Pain, Face recognition, Estimation, Training data, Gesture recognition BibRef

Vallez, N.[Noelia], Ruiz-Santaquiteria, J.[Jesus], Deniz, O.[Oscar], Hughes, J.[Jeff], Robertson, S.[Scott], Hoti, K.[Kreshnik], Bueno, G.[Gloria],
Adults' Pain Recognition via Facial Expressions Using CNN-Based AU Detection,
VIAAL22(15-27).
Springer DOI 2208
BibRef

Prajod, P.[Pooja], Huber, T.[Tobias], André, E.[Elisabeth],
Using Explainable AI to Identify Differences Between Clinical and Experimental Pain Detection Models Based on Facial Expressions,
MMMod22(I:311-322).
Springer DOI 2203
BibRef

Holowka, E.M.[Eileen Mary], Woods, S.[Sandra], Pahayahay, A.[Amber], Roy, M.[Mathieu], Khalili-Mahani, N.[Najmeh],
Principles for Designing an mHealth App for Participatory Research and Management of Chronic Pain,
DHM21(II:50-67).
Springer DOI 2108
BibRef

Szczapa, B.[Benjamin], Daoudi, M.[Mohamed], Berretti, S.[Stefano], Pala, P.[Pietro], del Bimbo, A.[Alberto], Hammal, Z.[Zakia],
Automatic Estimation of Self-Reported Pain by Interpretable Representations of Motion Dynamics,
ICPR21(2544-2550)
IEEE DOI 2105
Support vector machines, Manifolds, Symmetric matrices, Pain, Shape, Face recognition, Dynamics BibRef

Bellmann, P.[Peter], Lausser, L.[Ludwig], Kestler, H.A.[Hans A.], Schwenker, F.[Friedhelm],
Introducing Bidirectional Ordinal Classifier Cascades Based on a Pain Intensity Recognition Scenario,
MPRSS20(773-787).
Springer DOI 2103
BibRef

Carlini, L.P.[Lucas Pereira], Tamanaka, F.G.[Fernanda Goyo], Soares, J.C.A.[Juliana C. A.], Silva, G.V.T.[Giselle V. T.], Heideirich, T.M.[Tatiany M.], Balda, R.C.X.[Rita C. X.], Barros, M.C.M.[Marina C. M.], Guinsburg, R.[Ruth], Thomaz, C.E.[Carlos Eduardo],
Neonatal Pain Scales and Human Visual Perception: An Exploratory Analysis Based on Facial Expression Recognition and Eye-tracking,
CAIHA20(62-76).
Springer DOI 2103
BibRef

Wally, Y.[Youssef], Samaha, Y.[Yara], Yasser, Z.[Ziad], Walter, S.[Steffen], Schwenker, F.[Friedhelm],
Personalized k-fold Cross-validation Analysis with Transfer from Phasic to Tonic Pain Recognition on X-ITE Pain Database,
MPRSS20(788-802).
Springer DOI 2103
BibRef

Hinduja, S., Canavan, S., Yin, L.,
Recognizing Perceived Emotions from Facial Expressions,
FG20(236-240)
IEEE DOI 2102
Task analysis, Face recognition, Emotion recognition, Pain, expressions BibRef

Salekin, M.S., Zamzmi, G., Goldgof, D., Kasturi, R., Ho, T., Sun, Y.,
First Investigation into the Use of Deep Learning for Continuous Assessment of Neonatal Postoperative Pain,
FG20(415-419)
IEEE DOI 2102
Pain, Pediatrics, Visualization, Training, Feature extraction, Faces, Biomedical monitoring, Acute pain, Neonatal pain, Postoperative pain BibRef

Hinduja, S., Canavan, S., Kaur, G.,
Multimodal Fusion of Physiological Signals and Facial Action Units for Pain Recognition,
FG20(577-581)
IEEE DOI 2102
Physiology, Pain, Task analysis, Face recognition, Correlation, Blood pressure, Gold BibRef

Rasipuram, S., Sai, B.N., Jayagopi, D.B., Maitra, A.,
Using Deep 3D Features and an LSTM Based Sequence Model for Automatic Pain Detection in the Wild,
FG20(781-785)
IEEE DOI 2102
Pain, Videos, Face recognition, Feature extraction, Task analysis, Deep learning, automatic feature extraction BibRef

Xu, X., Sa, V.R.d.,
Exploring Multidimensional Measurements for Pain Evaluation using Facial Action Units,
FG20(786-792)
IEEE DOI 2102
Pain, Measurement, Gold, Predictive models, Training, Neural networks, Machine learning BibRef

Hummel, H.I., Pessanha, F., Salah, A.A., van Loon, T.J.P.A.M., Veltkamp, R.C.,
Automatic Pain Detection on Horse and Donkey Faces,
FG20(793-800)
IEEE DOI 2102
face recognition, image classification, image representation, pipelines, zoology, donkey faces, visible signs, affective computing BibRef

Huynh, V.T., Yang, H.J., Lee, G.S., Kim, S.H.,
Multimodality Pain and related Behaviors Recognition based on Attention Learning,
FG20(814-818)
IEEE DOI 2102
Pain, Feature extraction, Task analysis, Estimation, Face recognition, Visualization, Training, emopain, behaviors recognition BibRef

Li, Y., Ghosh, S., Joshi, J., Oviatt, S.,
LSTM-DNN based Approach for Pain Intensity and Protective Behaviour Prediction,
FG20(819-823)
IEEE DOI 2102
Pain, Task analysis, Training, Long short term memory, Deep learning, Terminology, Medical treatment, Chronic Pain, Protective Behavior, Neural Network BibRef

Yuan, X., Mahmoud, M.,
ALANet:Autoencoder-LSTM for pain and protective behaviour detection,
FG20(824-828)
IEEE DOI 2102
Pain, Task analysis, Feature extraction, Training, Data mining, Encoding, Deep learning BibRef

Mallol-Ragolta, A., Liu, S., Cummins, N., Schuller, B.,
A Curriculum Learning Approach for Pain Intensity Recognition from Facial Expressions,
FG20(829-833)
IEEE DOI 2102
Pain, Annotations, Feature extraction, Training, Videos, Computational modeling, Recurrent neural networks, Curriculum Learning BibRef

Lakshminarayan, S.A.S., Hinduja, S., Canavan, S.,
Three-level Training of Multi-Head Architecture for Pain Detection,
FG20(839-843)
IEEE DOI 2102
Pain, Feature extraction, Correlation, Training, Computer architecture, Task analysis, Cameras BibRef

Uddin, M.T.[Md Taufeeq], Canavan, S.[Shaun],
Quantified Facial Expressiveness for Affective Behavior Analytics,
WACV22(131-140)
IEEE DOI 2202
BibRef
Earlier:
Multimodal Multilevel Fusion for Sequential Protective Behavior Detection and Pain Estimation,
FG20(844-848)
IEEE DOI 2102
Benchmark testing, Task analysis, Facial features, Action and Behavior Recognition Biometrics -> Face Processing, Biometrics -> Human Motion Analysis/Capture. Pain, Estimation, Feature extraction, Electromyography, Metadata, Computational modeling, Training BibRef

Egede, J.O., Song, S., Olugbade, T.A., Wang, C., Williams, A.C.D.C., Meng, H., Aung, M., Lane, N.D., Valstar, M., Bianchi-Berthouze, N.,
EMOPAIN Challenge 2020: Multimodal Pain Evaluation from Facial and Bodily Expressions,
FG20(849-856)
IEEE DOI 2102
Pain, Face recognition, Task analysis, Muscles, Neck, Back, Training, Automatic Pain Assessment, Pain related Behaviour Analysis, Protective Movement behaviour Detection BibRef

Carlini, L.P.[Lucas Pereira], Soares, J.C.A.[Juliana C. A.], Silva, G.V.T.[Giselle V. T.], Heideirich, T.M.[Tatiany M.], Balda, R.C.X.[Rita C. X.], Barros, M.C.M.[Marina C. M.], Guinsburg, R.[Ruth], Thomaz, C.E.[Carlos Eduardo],
A Visual Perception Framework to Analyse Neonatal Pain in Face Images,
ICIAR20(I:233-243).
Springer DOI 2007
BibRef

Mauricio, A.[Antoni], Cappabianco, F.[Fábio], Veloso, A.[Adriano], Cámara, G.[Guillermo],
A Sequential Approach for Pain Recognition Based on Facial Representations,
CVS19(295-304).
Springer DOI 1912
BibRef

Yu, J.[Jun], Kurihara, T.[Toru], Zhan, S.[Shu],
Frame by Frame Pain Estimation Using Locally Spatial Attention Learning,
IbPRIA19(II:229-238).
Springer DOI 1910
BibRef

Yang, R., Hong, X., Peng, J., Feng, X., Zhao, G.,
Incorporating high-level and low-level cues for pain intensity estimation,
ICPR18(3495-3500)
IEEE DOI 1812
face recognition, health care, image representation, medical image processing, statistics, Histograms BibRef

Lopez-Martinez, D.[Daniel], Peng, K., Steele, S.C., Lee, A.J., Borsook, D., Picard, R.W.[Rosalind W.],
Multi-task multiple kernel machines for personalized pain recognition from functional near-infrared spectroscopy brain signals,
ICPR18(2320-2325)
IEEE DOI 1812
Pain, Task analysis, Kernel, Feature extraction, Training, Splines (mathematics), Machine learning BibRef

Tong, X.[Xin], Jin, W.[Weina], Cruz, K.[Kathryn], Gromala, D.[Diane], Garret, B.[Bernie], Taverner, T.[Tarnia],
A Case Study: Chronic Pain Patients' Preferences for Virtual Reality Games for Pain Distraction,
VAMR18(II: 3-11).
Springer DOI 1807
BibRef

Liu, P., Yazgan, I., Olsen, S., Moser, A., Ciftci, U., Bajwa, S., Tvetenstrand, C., Gerhardstein, P., Sadik, O., Yin, L.,
Clinical Valid Pain Database with Biomarker and Visual Information for Pain Level Analysis,
FG18(525-529)
IEEE DOI 1806
Blood, Correlation, Databases, Gold, Head, Pain, Video sequences, database, expression analysis BibRef

Haque, M.A., Bautista, R.B., Noroozi, F., Kulkarni, K., Laursen, C.B., Irani, R., Bellantonio, M., Escalera, S., Anbarjafari, G., Nasrollahi, K., Andersen, O.K., Spaich, E.G., Moeslund, T.B.,
Deep Multimodal Pain Recognition: A Database and Comparison of Spatio-Temporal Visual Modalities,
FG18(250-257)
IEEE DOI 1806
Face, Machine learning, Pain, Videos, Visual databases, Visualization, Database, Deep Learning, Depth, LSTM, Pain, RGB, RGBDT, Thermal, Video, Vision, multimodal BibRef

Thiam, P., Schwenker, F.,
Multi-modal data fusion for pain intensity assessment and classification,
IPTA17(1-6)
IEEE DOI 1804
electrocardiography, electromyography, feature extraction, medical signal processing, patient monitoring, sensor fusion, Signal Processing BibRef

Kessler, V., Thiam, P., Amirian, M., Schwenker, F.,
Pain recognition with camera photoplethysmography,
IPTA17(1-5)
IEEE DOI 1804
cardiology, electrocardiography, face recognition, feature extraction, image classification, webcam BibRef

Lu, Y., Mahmoud, M., Robinson, P.,
Estimating Sheep Pain Level Using Facial Action Unit Detection,
FG17(394-399)
IEEE DOI 1707
Animals, Ear, Face, Feature extraction, Gold, Pain, Taxonomy BibRef

Egede, J., Valstar, M., Martinez, B.,
Fusing Deep Learned and Hand-Crafted Features of Appearance, Shape, and Dynamics for Automatic Pain Estimation,
FG17(689-696)
IEEE DOI 1707
Estimation, Face, Feature extraction, Machine learning, Pain, Physiology, Shape BibRef

Zamzmi, G.[Ghada], Pai, C.Y.[Chih-Yun], Goldgof, D.[Dmitry], Kasturi, R.[Rangachar], Sun, Y.[Yu], Ashmeade, T.[Terri],
Automated Pain Assessment in Neonates,
SCIA17(II: 350-361).
Springer DOI 1706
BibRef

Zamzmi, G., Pai, C.Y., Goldgof, D., Kasturi, R., Ashmeade, T., Sun, Y.,
An approach for automated multimodal analysis of infants' pain,
ICPR16(4148-4153)
IEEE DOI 1705
Biomedical monitoring, Feature extraction, Optical imaging, Pain, Pediatrics, Physiology, Strain BibRef

Yang, R., Tong, S., Bordallo, M., Boutellaa, E., Peng, J., Feng, X., Hadid, A.,
On pain assessment from facial videos using spatio-temporal local descriptors,
IPTA16(1-6)
IEEE DOI 1703
emotion recognition BibRef

Zhou, J., Hong, X., Su, F., Zhao, G.,
Recurrent Convolutional Neural Network Regression for Continuous Pain Intensity Estimation in Video,
Affect16(1535-1543)
IEEE DOI 1612
BibRef

Saeijs, R.W.J.J., Tjon a Ten, W.E., de With, P.H.N.,
Dual-camera 3D head tracking for clinical infant monitoring,
ISCV18(1-8)
IEEE DOI 1807
BibRef
Earlier:
Dense-Hog-based 3D face tracking for infant pain monitoring,
ICIP16(1719-1723)
IEEE DOI 1610
cameras, face recognition, feature extraction, image sequences, medical image processing, object detection, object tracking, infant monitoring. BibRef

Li, C., Zinger, S., Tjon a Ten, W.E., de With, P.H.N.,
Video-based discomfort detection for infants using a Constrained Local Model,
WSSIP16(1-4)
IEEE DOI 1608
face recognition BibRef

Pence, T.B.[Toni B.], Dukes, L.C.[Lauren C.], Hodges, L.F.[Larry F.],
Animation Validation of Obese Virtual Pediatric Patients Using a FLACC Pain Scale,
VAMR16(552-564).
Springer DOI 1608
BibRef

Lundtoft, D.H.[Dennis H.], Nasrollahi, K.[Kamal], Moeslund, T.B.[Thomas B.], Escalera, S.[Sergio],
Spatiotemporal Facial Super-Pixels for Pain Detection,
AMDO16(34-43).
Springer DOI 1608
BibRef

Liu, Z.J.[Zhe-Jun], Wangluo, S.[Sijia], Dong, H.[Hua],
Advances and Tendencies: A Review of Recent Studies on Virtual Reality for Pain Management,
VAMR16(512-520).
Springer DOI 1608
BibRef

Irani, R.[Ramin], Nasrollahi, K.[Kamal], Moeslund, T.B.[Thomas B.],
Pain recognition using spatiotemporal oriented energy of facial muscles,
ChaLearn15(80-87)
IEEE DOI 1510
Energy measurement BibRef

Irani, R.[Ramin], Nasrollahi, K.[Kamal], Simon, M.O.[Marc O.], Corneanu, C.A.[Ciprian A.], Escalera, S.[Sergio], Bahnsen, C.[Chris], Lundtoft, D.H.[Dennis H.], Moeslund, T.B.[Thomas B.], Pedersen, T.L.[Tanja L.], Klitgaard, M.L.[Maria-Louise], Petrini, L.[Laura],
Spatiotemporal analysis of RGB-D-T facial images for multimodal pain level recognition,
ChaLearn15(88-95)
IEEE DOI 1510
Calibration BibRef

Pedersen, H.[Henrik],
Learning Appearance Features for Pain Detection Using the UNBC-McMaster Shoulder Pain Expression Archive Database,
CVS15(128-136).
Springer DOI 1507
BibRef

Zhang, X.[Xing], Yin, L.J.[Li-Jun], Cohn, J.F.,
Three dimensional binary edge feature representation for pain expression analysis,
FG15(1-7)
IEEE DOI 1508
emotion recognition BibRef

Florea, C.[Corneliu], Florea, L.[Laura], Vertan, C.[Constantin],
Learning Pain from Emotion: Transferred HoT Data Representation for Pain Intensity Estimation,
ACVR14(778-790).
Springer DOI 1504
BibRef

Werner, P.[Philipp], Al-Hamadi, A.[Ayoub], Walter, S.[Steffen], Gruss, S.[Sascha], Traue, H.C.[Harald C.],
Automatic heart rate estimation from painful faces,
ICIP14(1947-1951)
IEEE DOI 1502
Electrocardiography BibRef

Werner, P.[Philipp], Al-Hamadi, A.[Ayoub], Niese, R.[Robert], Walter, S.[Steffen], Gruss, S.[Sascha], Traue, H.C.[Harald C.],
Automatic Pain Recognition from Video and Biomedical Signals,
ICPR14(4582-4587)
IEEE DOI 1412
Data integration BibRef

Zafar, Z.[Zuhair], Khan, N.A.[Nadeem Ahmad],
Pain Intensity Evaluation through Facial Action Units,
ICPR14(4696-4701)
IEEE DOI 1412
Databases BibRef

Zaker, N., Mahoor, M.H., Mattson, W.I., Messinger, D.S., Cohn, J.F.,
A comparison of alternative classifiers for detecting occurrence and intensity in spontaneous facial expression of infants with their mothers,
FG13(1-6)
IEEE DOI 1309
eigenvalues and eigenfunctions BibRef

Reale, M., Zhang, X.[Xing], Yin, L.J.[Li-Jun],
Nebula feature: A space-time feature for posed and spontaneous 4D facial behavior analysis,
FG13(1-8)
IEEE DOI 1309
curvature measurement BibRef

Werner, P.[Philipp], Al-Hamadi, A.[Ayoub], Niese, R.[Robert],
Pain recognition and intensity rating based on Comparative Learning,
ICIP12(2313-2316).
IEEE DOI 1302
BibRef

Chapter on Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics continues in
Three-Dimensional Face Expression Recognition and Analysis .


Last update:Nov 26, 2024 at 16:40:19