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?
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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
Williams, A. C. D. C. (1995) · Journal of Psychosomatic Research · doi:10.1016/0022-3999(95)90016-0 · Cited by 1,472
Sierles, F. S. (1982) · Springer eBooks · doi:10.1007/978-94-011-7973-7_4 · Cited by 992
Li, S. Z., & Jain, A. K. (2005) · Springer eBooks · doi:10.1007/b138828 · Cited by 799
Recognizing Facial Expression: Machine Learning and Application to Spontaneous Behavior
Bartlett, M. S., Littlewort, G. C., Frank, M. G., Lainscsek, C. S. M., Fasel, I., & Movellan, J. R. (2005) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2005.297 · Cited by 643
Pantic, M., & Patras, I. (2006) · IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) · doi:10.1109/tsmcb.2005.859075 · Cited by 580
The consistency of facial expressions of pain: a comparison across modalities
Prkachin, K. M. (1992) · Pain · doi:10.1016/0304-3959(92)90213-u · Cited by 553
Self-citationHandbook of methods in nonverbal behavior research
Scherer, K. R., & Ekman, P. (1982) · Cited by 521
Prkachin, K. M., & Solomon, P. E. (2008) · Pain · doi:10.1016/j.pain.2008.04.010 · Cited by 472
Self-citationSimple pain rating scales hide complex idiosyncratic meanings
Williams, A. C. D. C., Davies, H. T. O., & Chadury, Y. (2000) · Pain · doi:10.1016/s0304-3959(99)00299-7 · Cited by 450
Facial Action Recognition for Facial Expression Analysis From Static Face Images
Pantić, M., & Rothkrantz, L. J. M. (2004) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2004.825931 · Cited by 425
Tian, Y., Takeo Kanade, A., & Cohn, J. F. (2005) · Springer eBooks · doi:10.1007/0-387-27257-7_12 · Cited by 407
Self-citationObserver-Based Measurement of Facial Expression with the Facial Action Coding System
Cohn, J. F., Ambadar, Z., & Ekman, P. (2007) · Oxford University Press eBooks · doi:10.1093/oso/9780195169157.003.0014 · Cited by 402
Self-citationFacial Action Unit Recognition by Exploiting Their Dynamic and Semantic Relationships
Tong, Y., Liao, W., & Ji, Q. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1094 · Cited by 380
THE TIMING OF FACIAL MOTION IN POSED AND SPONTANEOUS SMILES
Cohn, J. F., & Schmidt, K. L. (2004) · International Journal of Wavelets Multiresolution and Information Processing · doi:10.1142/s021969130400041x · Cited by 308
Self-citationThe Science of Emotion: Research and Tradition in the Psychology of Emotion
Cornelius, R. R. (1995) · Medical Entomology and Zoology · Cited by 302
Fully Automatic Facial Action Recognition in Spontaneous Behavior
Bartlett, M. S., Littlewort, G. C., Frank, M. G., Lainscsek, C. S. M., Fasel, I., & Movellan, J. R. (2006) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fgr.2006.55 · Cited by 295
The facial expression of pain.
Craig, K. D., Prkachin, K. M., & GRUNAU, R. E. (2001) · Cited by 259
Self-citationTakeo Kanade, A. (2005) · Lecture notes in computer science · doi:10.1007/11564386_1 · Cited by 226
Gibson, R. (2002) · Cited by 195
Machine learning methods for fully automatic recognition of facial expressions and facial actions
Bartlett, M. S., Littlewort, G. C., Lainscsek, C. S. M., Fasel, I., & Movellan, J. R. (2005) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2004.1398364 · Cited by 171
Spontaneous vs. posed facial behavior: automatic analysis of brow actions
Valstar, M. F., Pantic, M., Ambadar, Z., & Cohn, J. F. (2006) · Proceedings of the 8th international conference on Multimodal interfaces · doi:10.1145/1180995.1181031 · Cited by 168
Self-citationInvestigating Spontaneous Facial Action Recognition through AAM Representations of the Face
Lucey, S., Bilal, A., & F., J. (2007) · Face Recognition · doi:10.5772/4841 · Cited by 161
Self-citationHuman computing and machine understanding of human behavior
Pantić, M., Pentland, A., Nijholt, A., & Huang, T. S. (2006) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/1180995.1181044 · Cited by 157
Littlewort, G. C., Bartlett, M. S., & Lee, K. (2007) · International Conference on Multimodal Interfaces · doi:10.1145/1322192.1322198 · Cited by 152
Encoding and decoding of pain expressions: a judgement study
Prkachin, K. M., Berzins, S. A., & Mercer, S. R. (1994) · Pain · doi:10.1016/0304-3959(94)90206-2 · Cited by 127
Self-citationAshraf, A. B., Lucey, S., Cohn, J. F., Chen, T., Ambadar, Z., Prkachin, K., Solomon, P., & Theobald, B. J. (2007) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/1322192.1322197 · Cited by 108
Self-citationDetecting Facial Actions and their Temporal Segments in Nearly Frontal-View Face Image Sequences
Pantić, M., & Patras, I. (2006) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2005.1571665 · Cited by 93
2D vs. 3D Deformable Face Models: Representational Power, Construction, and Real-Time Fitting
Matthews, I. A., Xiao, J., & Baker, S. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-007-0043-2 · Cited by 92
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
Facing Imbalanced Data--Recommendations for the Use of Performance Metrics
Jeni, L. A., Cohn, J. F., & de la Torre, F. N. (2013) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2013.47 · Cited by 828
Self-citationPainful data: The UNBC-McMaster shoulder pain expression archive database
Lucey, P., Cohn, J. F., Prkachin, K. M., Solomon, P. E., & Matthews, I. (2011) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2011.5771462 · Cited by 634
Self-citationAffective Body Expression Perception and Recognition: A Survey
Kleinsmith, A., & Bianchi‐Berthouze, N. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2012.16 · Cited by 624
Automatic facial expression recognition using features of salient facial patches
Happy, S. L., & Routray, A. (2014) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2014.2386334 · Cited by 608
Costa, E. D., Minero, M., Lebelt, D., Stucke, D., Canali, E., & Leach, M. C. (2014) · PLoS ONE · doi:10.1371/journal.pone.0092281 · Cited by 641
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
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