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
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ISVC07(I: 509-520).
Springer DOI
0711
BibRef
Hammal, Z.,
Couvreur, L.,
Caplier, A.,
Rombaut, M.,
Facial Expression Recognition Based on the Belief Theory:
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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
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Dhall, A.[Abhinav],
Bartlett, M.S.[Marian Stewart],
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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
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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
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Rathee, N.[Neeru],
Ganotra, D.[Dinesh],
Multiview Distance Metric Learning on facial feature descriptors for
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CVIU(147), No. 1, 2016, pp. 77-86.
Elsevier DOI
1605
Multiview Distance Metric Learning
BibRef
Kaltwang, S.[Sebastian],
Todorovic, S.,
Pantic, M.[Maja],
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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],
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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
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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
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Lo Presti, L.[Liliana],
La Cascia, M.[Marco],
Boosting Hankel matrices for face emotion recognition and pain
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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
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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
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IEICE(E101-D), No. 6, June 2018, pp. 1572-1580.
WWW Link.
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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:
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IEICE(E101-D), No. 7, July 2018, pp. 1860-1869.
WWW Link.
1807
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Virrey, R.A.[Reneiro Andal],
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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
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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
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Orihuela-Espina, F.[Felipe],
Palafox, L.[Lorena],
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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
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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
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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
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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
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 .