21.3.6.1.6 Face Expression Recognition for Pain

Chapter Contents (Back)
Faces, Expression. Facial 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; Computer vision; 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., Williams, A.C.d.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

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

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


Tavakolian, M., Hadid, A.,
Deep Binary Representation of Facial Expressions: A Novel Framework for Automatic Pain Intensity Recognition,
ICIP18(1952-1956)
IEEE DOI 1809
Pain, Feature extraction, Binary codes, Databases, Face, Hamming distance, Estimation, Pain Assessment, Clinical Diagnosis 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

Wang, F., Xiang, X., Liu, C., Tran, T.D., Reiter, A., Hager, G.D., Quon, H., Cheng, J., Yuille, A.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

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 12, 2018 at 11:26:54