The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
The painful face – Pain expression recognition using active appearance models
Open accessAbstract
Pain is typically assessed by patient self-report. Self-reported pain, however, is difficult to interpret and may be impaired or in some circumstances (i.e., young children and the severely ill) not even possible. To circumvent these problems behavioral scientists have identified reliable and valid facial indicators of pain. Hitherto, these methods have required manual measurement by highly skilled human observers. In this paper we explore an approach for automatically recognizing acute pain without the need for human observers. Specifically, our study was restricted to automatically detecting pain in adult patients with rotator cuff injuries. The system employed video input of the patients as they moved their affected and unaffected shoulder. Two types of ground truth were considered. Sequence-level ground truth consisted of Likert-type ratings by skilled observers. Frame-level ground truth was calculated from presence/absence and intensity of facial actions previously associated with pain. Active appearance models (AAM) were used to decouple shape and appearance in the digitized face images. Support vector machines (SVM) were compared for several representations from the AAM and of ground truth of varying granularity. We explored two questions pertinent to the construction, design and development of automatic pain detection systems. First, at what level (i.e., sequence- or frame-level) should datasets be labeled in order to obtain satisfactory automatic pain detection performance? Second, how important is it, at both levels of labeling, that we non-rigidly register the face?
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Seel, N. M. (2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_5195 · Cited by 19,482
Robust Real-Time Face Detection
Viola, P., & Jones, M. J. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000013087.49260.fb · Cited by 14,097
The expression of the emotions in man and animals.
Darwin, C. D. (1872) · J. Murray eBooks · doi:10.1037/10001-000 · Cited by 12,120
Russell, E. L., Chiang, L. H., & Braatz, R. D. (2000) · Advances in industrial control · doi:10.1007/978-1-4471-0409-4_3 · Cited by 7,938
Ekman, P., & Friesen, W. V. (2019) · PsycTESTS Dataset · doi:10.1037/t27734-000 · Cited by 6,019
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
A Practical Guide to Support Vector Classication
Hsu, C., Chang, C.-C., & Lin, C. (2008) · Cited by 5,056
Benjamin, S. (1994) · Journal of Psychosomatic Research · doi:10.1016/0022-3999(94)90012-4 · Cited by 2,001
Li, S. Z., Jain, A. K., Deng, J., & service), S. (. (2011) · Springer eBooks · doi:10.1007/978-0-85729-932-1 · Cited by 1,867
Active Appearance Models Revisited
Matthews, I., & Baker, S. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029666.37597.d3 · Cited by 1,538
Flagged first, then by citations
Research works printed but not linked: 1
- T. Hadjistavropoulos, K.D. Craig, Social influences and the communication of pain, in: Pain: Psychological Perspectives, Erbaulm, Newyork, 2004, pp. 87–112.
Cited by
Sorted by citations
Items 101–200 of 355
Chen, Z., Ansari, R., & Wilkie, D. J. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2949314 · Cited by 31
Automatic Pain Detection on Horse and Donkey Faces
Hummel, H. I., Pessanha, F., Salah, A. A., van Loon, J. P. A. M., & Veltkamp, R. C. (2020) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg47880.2020.00114 · Cited by 30
Pain recognition and intensity rating based on Comparative Learning
Werner, P., Al-Hamadi, A. K., & Niese, R. (2012) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2012.6467359 · Cited by 31
Automatic Facial Expression Recognition
Whitehill, J., Bartlett, M. S., & Movellan, J. R. (2013) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195387643.003.0007 · Cited by 30
Tavakolian, M., & Hadid, A. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451681 · Cited by 30
A dynamic multimodal approach for assessing learners' interaction experience
Jraidi, I., Chaouachi, M., & Frasson, C. (2013) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2522848.2522896 · Cited by 29
Boosting Hankel matrices for face emotion recognition and pain detection
Presti, L. L., & Cascia, M. L. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.10.007 · Cited by 30
Generalized Dictionaries for Multiple Instance Learning
Shrivastava, A., Patel, V. M., Pillai, J. K., & Chellappa, R. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0831-z · Cited by 28
A Modular System for Detection, Tracking and Analysis of Human Faces in Thermal Infrared Recordings
Kopaczka, M., Breuer, L., Schock, J., & Merhof, D. (2019) · Sensors · doi:10.3390/s19194135 · Cited by 28
Form as a Cue in the Automatic Recognition of Non-acted Affective Body Expressions
Kleinsmith, A., & Bianchi‐Berthouze, N. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24600-5_19 · Cited by 28
Bartlett, M. S., & Whitehill, J. (2011) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199559053.013.0025 · Cited by 27
Recognition of emotions from video using acoustic and facial features
Rao, K. S., & Koolagudi, S. G. (2013) · Signal Image and Video Processing · doi:10.1007/s11760-013-0522-6 · Cited by 27
Automated Pain Detection in Facial Videos of Children Using Human-Assisted Transfer Learning
Xu, X., Craig, K. D., Díaz, D., Goodwin, M. S., Akçakaya, M., Susam, B. T., Huang, J. S., & de Sa, V. R. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-12738-1_12 · Cited by 27
Survey on Pain Detection Using Machine Learning Models: Narrative Review
Fang, R., Hosseini, E., Zhang, R., Fang, C., Rafatirad, S., & Homayoun, H. (2024) · JMIR AI · doi:10.2196/53026 · Cited by 27
Exploring Multidimensional Measurements for Pain Evaluation using Facial Action Units
Xu, X., & de Sa, V. R. (2020) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg47880.2020.00087 · Cited by 26
Spectral embedding based facial expression recognition with multiple features
Yu, K., Wang, Z., Hagenbuchner, M., & Feng, D. D. (2013) · Neurocomputing · doi:10.1016/j.neucom.2013.09.046 · Cited by 26
Closed-loop control for intensive care unit sedation
Haddad, W. M., & Bailey, J. M. (2009) · Best practice & research. Clinical anaesthesiology · doi:10.1016/j.bpa.2008.07.007 · Cited by 26
Machine learning methods for automatic pain assessment using facial expression information
Liu, D., Cheng, D., Houle, T. T., Chen, L. L., Zhang, W., & Deng, H. (2018) · Medicine · doi:10.1097/md.0000000000013421 · Cited by 24
Emotion Recognition on Call Center Voice Data
Yurtay, Y., Demirci, H., TİRYAKİ, H. T., & Altun, T. (2024) · Applied Sciences · doi:10.3390/app14209458 · Cited by 26
Automatic classification of human facial features based on their appearance
Fuentes-Hurtado, F., Diego-Más, J. A., Naranjo, V., & Alcañíz, M. (2019) · PLoS ONE · doi:10.1371/journal.pone.0211314 · Cited by 25
Pain intensity estimation based on a spatial transformation and attention CNN
Xin, X., Lin, X., Yang, S.-F., & Zheng, X. (2020) · PLoS ONE · doi:10.1371/journal.pone.0232412 · Cited by 27
A Novel Automatic Facial Expression Recognition Method Based on AAM
Wang, L., Li, R.-F., & Wang, K. (2014) · Journal of Computers · doi:10.4304/jcp.9.3.608-617 · Cited by 24
Neonatal Pain Expression Recognition Using Transfer LearningPreprint
Zamzmi, G., Goldgof, D. B., Kasturi, R., & Sun, Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.01631 · Cited by 25
Fisher, A. S., Lanigan, M. T., Upton, N., & Lione, L. A. (2021) · Frontiers in Pharmacology · doi:10.3389/fphar.2020.614990 · Cited by 28
Explainable AI (XAI) Applied in Machine Learning for Pain Modeling: A Review
Madanu, R., Abbod, M., Hsiao, F., Chen, W., & Shieh, J.-S. (2022) · Technologies · doi:10.3390/technologies10030074 · Cited by 28
A common framework for real-time emotion recognition and facial action unit detection
Gehrig, T., & Ekenel, H. K. (2011) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2011.5981817 · Cited by 24
Faces of clinical pain: Inter‐individual facial activity patterns in shoulder pain patients
Miriam Kunz, A., Prkachin, K. M., Solomon, P. E., & Lautenbacher, S. (2020) · European Journal of Pain · doi:10.1002/ejp.1691 · Cited by 24
Self-citationPain-attentive network: a deep spatio-temporal attention model for pain estimation
Huang, D. J., Xia, Z., Mwesigye, J., & Feng, X. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09397-1 · Cited by 25
Jraidi, I., Chaouachi, M., & Frasson, C. (2014) · Advances in Human-Computer Interaction · doi:10.1155/2014/632630 · Cited by 22
Rathee, N., & Ganotra, D. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2015.12.004 · Cited by 23
A deep learning-based comprehensive robotic system for lower limb rehabilitation
Mukherjee, P., & Roy, A. H. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.107178 · Cited by 22
Pain-awareness multistream convolutional neural network for pain estimation
Huang, D. J., Xia, Z., Li, L., Wang, K., & Feng, X. (2019) · Journal of Electronic Imaging · doi:10.1117/1.jei.28.4.043008 · Cited by 24
Pain Detection from Facial Videos Using Two-Stage Deep Learning
Menchetti, G., Chen, Z., Wilkie, D. J., Ansari, R., Yardımcı, Y., & Çetin, A. E. (2019) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip45357.2019.8969274 · Cited by 22
Zhang, L., Tjondronegoro, D., Chandran, V., & Eggink, J. (2015) · Multimedia Tools and Applications · doi:10.1007/s11042-015-2497-5 · Cited by 22
AM-FED+: An Extended Dataset of Naturalistic Facial Expressions Collected in Everyday Settings
McDuff, D., Amr, M., & el Kaliouby, R. (2018) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2801311 · Cited by 22
Pain Intensity Estimation Using Deep Spatiotemporal and Handcrafted Features
Jinwei, W. A. N. G., & Sun, H. (2018) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2017edp7318 · Cited by 22
Spatio-Temporal Pain Estimation Network With Measuring Pseudo Heart Rate Gain
Huang, D. J., Feng, X., Zhang, H., Yu, Z., Peng, J., Zhao, G., & Xia, Z. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3096080 · Cited by 21
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
Self-citationFacial action unit detection using kernel partial least squares
Gehrig, T., & Ekenel, H. K. (2011) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2011.6130506 · Cited by 21
Automated detection of pain from facial expressions: a rule-based approach using AAM
Chen, Z., Ansari, R., & Wilkie, D. J. (2012) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.912537 · Cited by 21
Prkachin, K. M. (2011) · Pain Management · doi:10.2217/pmt.11.22 · Cited by 21
Self-citation2D 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
Pain assessment using the Facial Action Coding System. A systematic review
Rojo, R., Prados‐Frutos, J. C., & López‐Valverde, A. (2015) · Medicina Clínica (English Edition) · doi:10.1016/j.medcle.2014.08.002 · Cited by 20
Pain Level Detection From Facial Image Captured by Smartphone
Hasan, M. K., Ahsan, G. M. T., Ahamed, S. I., Love, R., & Salim, R. (2016) · Journal of Information Processing · doi:10.2197/ipsjjip.24.598 · Cited by 20
Facial Pain Expression Recognition in Real-Time Videos
Dutta, P., & Nachamai, M. (2018) · Journal of Healthcare Engineering · doi:10.1155/2018/7961427 · Cited by 20
Detecting discomfort in infants through facial expressions
Sun, Y., Shan, C., Tan, T., Tong, T., Wang, W., Pourtaherian, A., & de With, P. H. N. (2019) · Physiological Measurement · doi:10.1088/1361-6579/ab55b3 · Cited by 20
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
Spontaneous Pain Expression Recognition in Video Sequences
Hammal, Z., Miriam Kunz, A., Arguin, M., & Gosselin, F. (2008) · Electronic workshops in computing · doi:10.14236/ewic/vocs2008.17 · Cited by 20
Assessment of postoperative pain in children with computer assisted facial expression analysis
Aydın, A. İ., & Özyazıcıoğlu, N. (2023) · Journal of Pediatric Nursing · doi:10.1016/j.pedn.2023.03.008 · 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
Video-based discomfort detection for infants
Sun, Y., Shan, C., Tan, T., Long, X., Pourtaherian, A., Zinger, S., & de With, P. H. N. (2018) · Machine Vision and Applications · doi:10.1007/s00138-018-0968-1 · Cited by 19
Craig, K. D. (2020) · Paediatric and Neonatal Pain · doi:10.1002/pne2.12034 · Cited by 19
Zhang, S., & Seymour, B. (2014) · Current Biology · doi:10.1016/j.cub.2014.07.010 · Cited by 19
Danelakis, A., Theoharis, T., & Pratikakis, I. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-5699-9 · Cited by 19
Registration Invariant Representations for Expression Detection
Lucey, P., Lucey, S., & Cohn, J. F. (2010) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2010.53 · Cited by 18
Self-citationDynamic Facial Expression Feature Learning Based on Sparse RNN
Zhi, R., & Wan, M. (2019) · IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC) · doi:10.1109/itaic.2019.8785844 · Cited by 20
Convolution Neural Network for Pain Intensity Assessment from Facial Expression
Hosseini, E., Fang, R., Zhang, R., Chuah, C., Orooji, M., Rafatirad, S., Rafatirad, S., & Homayoun, H. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871770 · Cited by 18
Enforcing Multilabel Consistency for Automatic Spatio-Temporal Assessment of Shoulder Pain Intensity
Erekat, D., Hammal, Z., Siddiqui, M., & Dibeklioğlu, H. (2020) · Companion Publication of the International Conference on Multimodal Interaction (ICMI Companion) · doi:10.1145/3395035.3425190 · Cited by 18
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
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
The Good Our Field Can Hope to Do, the Harm It Should Avoid
Cowie, R. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2012.40 · Cited by 17
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
Self-citationRash, J. A., Prkachin, K. M., Solomon, P. E., & Campbell, T. S. (2019) · European Journal of Pain · doi:10.1002/ejp.1369 · Cited by 16
Self-citationPersonalized 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
Self-citationPain 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
Identifying User-Specific Facial Affects from Spontaneous Expressions with Minimal Annotation
Huang, M. X., Ngai, G., Hua, K. A., Chan, S. C. F., & Leong, H. V. (2015) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2015.2495222 · 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
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
Inducing bias modulates sensitivity to nonverbal cues of others' pain
Pronina, I., & Rule, N. O. (2014) · European Journal of Pain · doi:10.1002/ejp.510 · Cited by 16
Gaze direction estimation by component separation for recognition of Eye Accessing Cues
Vrânceanu, R., Florea, C., Florea, L., & Vertan, C. (2015) · Machine Vision and Applications · doi:10.1007/s00138-014-0656-8 · Cited by 16
Detecting demeanor for healthcare with machine learning
Healy, M., & Walsh, P. (2017) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2017.8217970 · 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
Challenges and Emerging Trends for Machine Reading of the Mind from Facial Expressions
Ghazouani, H. (2023) · SN Computer Science · doi:10.1007/s42979-023-02447-z · Cited by 15
Park, I., Park, J. H., Yoon, J., Na, H., Oh, A., Ryu, J., & Koo, B. (2024) · Korean Journal of Anesthesiology · doi:10.4097/kja.23583 · Cited by 18
Computational diagnostic methods on 2D photographs: A review of the literature
Hennocq, Q., Khonsari, R. H., Benoît, V., Rio, M., & Garcelon, N. (2021) · Journal of Stomatology Oral and Maxillofacial Surgery · doi:10.1016/j.jormas.2021.04.003 · Cited by 15
Pain Intensity Recognition from Masked Facial Expressions using Swin-Transformer
Yuan, X., Zhang, S., Zhao, C., He, X., Ouyang, B., & Yang, S. (2022) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio55434.2022.10011731 · Cited by 15
Seshadri, K., & Savvides, M. (2012) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2012.2195175 · Cited by 15
Evaluación del dolor mediante el Sistema de Codificación de la Acción Facial. Revisión sistemática
Rojo, R., Prados‐Frutos, J. C., & López‐Valverde, A. (2014) · Medicina Clínica · doi:10.1016/j.medcli.2014.08.010 · 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
Group Emotion Recognition Based on Global and Local Features
Dai, Y., Liu, X., Shuzhan, D., & Yang, L. (2019) · IEEE Access · doi:10.1109/access.2019.2932797 · Cited by 15
A novel facial expression recognition method based on extreme learning machine
Liu, Z., Sui, G.-T., Li, D., & Tan, G. (2015) · Chinese Control Conference (CCC) · doi:10.1109/chicc.2015.7260233 · Cited by 14
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
Emotion information visualization through learning of 3D morphable face model
Jin, H., Wang, X., Lian, Y., & Hua, J. (2018) · The Visual Computer · doi:10.1007/s00371-018-1482-1 · Cited by 15
Real-Time Facial Emotion Recognition Framework for Employees of Organizations Using Raspberry-Pi
Rathour, N., Khanam, Z., Gehlot, A., Singh, R., Rashid, M. U., AlGhamdi, A. S., & Alshamrani, S. S. (2021) · Applied Sciences · doi:10.3390/app112210540 · Cited by 14
Prediction of pain intensity using multimedia data
Singh, S. K., Tiwari, S., Abidi, A. I., & Singh, A. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-4718-6 · Cited by 14
Incorporating high-level and low-level cues for pain intensity estimation
Yang, R., Hong, X., Peng, J., Feng, X., & Zhao, G. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8545244 · Cited by 14
Multimodal-Based Stream Integrated Neural Networks for Pain Assessment
Zhi, R., Zhou, C., Junwei, Y. U., Li, T., & Zamzmi, G. (2021) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2021edp7065 · Cited by 13
Wu, J., Shi, Y., Tang, Y., Ren, W., & Yan, H. (2024) · Journal of Electronic Science and Technology · doi:10.1016/j.jnlest.2024.100260 · Cited by 13
Ghosh, A. K., Umer, S., Dhara, B. C., & Ali, G. G. M. N. (2025) · Sensors · doi:10.3390/s25041223 · Cited by 13
Pain Expression Recognition Based on pLSA Model
Zhu, S. (2014) · The Scientific World JOURNAL · doi:10.1155/2014/736106 · Cited by 13
Learning Facial Expressions with 3D Mesh Convolutional Neural Network
Jin, H., Lian, Y., & Hua, J. (2018) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3200572 · 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
Browne, M. E., Hadjistavropoulos, T., Prkachin, K. M., Ashraf, A. B., & Taati, B. (2019) · Journal of Nonverbal Behavior · doi:10.1007/s10919-019-00303-4 · Cited by 13
Self-citationCumulative attributes for pain intensity estimation
Egede, J., & Valstar, M. (2017) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3136755.3136789 · Cited by 12
Intelligent Appearance and shape based facial emotion recognition for a humanoid robot
Mistry, K., Li, Z., Neoh, S. C., Jiang, M. X., Hossain, A., & Lafon, B. (2014) · International Conference on Software, Knowledge, Information Management and Applications (SKIMA) · doi:10.1109/skima.2014.7083542 · Cited by 12
Triage through telemedicine in paediatric emergency care—Results of a concordance study
Beyer, A., Moon, K., Penndorf, P., Hirsch, T., Zahn-Tesch, U., Hoffmann, W. U., Lode, H. N., & van den Berg, N. (2022) · PLoS ONE · doi:10.1371/journal.pone.0269058 · Cited by 11
Yue, J.-M., Wang, Q., Liu, B., & Zhou, L. (2024) · World Journal of Clinical Cases · doi:10.12998/wjcc.v12.i4.681 · Cited by 11
PLAAN: Pain Level Assessment with Anomaly-detection based Network
Li, Y., Ghosh, S., & Joshi, J. (2021) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-020-00362-8 · Cited by 12
Metadata
- Authors
- Ashraf, A. B., Lucey, S., Cohn, J. F., Chen, T., Ambadar, Z., Prkachin, K. M., & Solomon, P. E.
- Affiliations
- University of Pittsburgh (US)
- Corresponding author
- Cohn, Jeffrey F.
- Venue
- Image and Vision Computing
- Publisher
- Elsevier BV
- Published
- 2009-11
- Volume
- 27
- Issue
- 12
- Pages
- 1788-1796
- ISSN
- 0262-8856 (ISSN-L)
- PMC
- PMC3402903
- OpenAlex
- W2090495691
- Cited by
- 355
- References
- 38
- Citations by year
- 2026: 1 · 2025: 9 · 2024: 14 · 2023: 7 · 2022: 11 · 2021: 29 · 2020: 28 · 2019: 31 · 2018: 31 · 2017: 26 · 2016: 33 · 2015: 25 · 2014: 28 · 2013: 20 · 2012: 21
- FWCI
- 21.67
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Image and Vision Computing (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- National University of Singapore (submittedVersion)
- Funders
- University of Northern British Columbia
- McMaster University
- Canadian Institutes of Health Research (MOP77799)
- National Institute of Mental Health (MH-51435, R01MH051435)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- active appearance models, rotator cuff injury, facial action units, support vector machine, ground truth annotation, video-based pain assessment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
