Automatically Detecting Pain in Video Through Facial Action Units
Open accessAbstract
In a clinical setting, pain is reported either through patient self-report or via an observer. Such measures are problematic as they are: 1) subjective, and 2) give no specific timing information. Coding pain as a series of facial action units (AUs) can avoid these issues as it can be used to gain an objective measure of pain on a frame-by-frame basis. Using video data from patients with shoulder injuries, in this paper, we describe an active appearance model (AAM)-based system that can automatically detect the frames in video in which a patient is in pain. This pain data set highlights the many challenges associated with spontaneous emotion detection, particularly that of expression and head movement due to the patient's reaction to pain. In this paper, we show that the AAM can deal with these movements and can achieve significant improvements in both the AU and pain detection performance compared to the current-state-of-the-art approaches which utilize similarity-normalized appearance features only.
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Pain detection is used for monitoring patient progress in clinical settings.
“Pain detection is used for monitoring patient progress in clinical settings [48], [49], [50].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [48]
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References 18 · Cited by 267
- Authors
- Lucey, P., Cohn, J. F., Matthews, I., Lucey, S., Sridharan, S., Howlett, J., & Prkachin, K. M.
- Affiliations
- University of Pittsburgh (US)
- Walt Disney (United States) (US)
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Queensland University of Technology (AU)
- University of Northern British Columbia (CA)
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
- Published
- 2011-06
- Volume
- 41
- Issue
- 3
- Pages
- 664-674
- ISSN
- 1083-4419 (ISSN-L)
- 1941-0492
- PMC
- PMC6942457
- OpenAlex
- W260234364
- Cited by
- 267
- References
- 18
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 18 · 2023: 14 · 2022: 15 · 2021: 23 · 2020: 22 · 2019: 28 · 2018: 26 · 2017: 26 · 2016: 27 · 2015: 21 · 2014: 11 · 2013: 11 · 2012: 6
- FWCI
- 8.79
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Faculty of Built Environment and Engineering
- Funders
- Canadian Institutes of Health Research (MOP77799)
- National Institute of Mental Health (R01MH051435, R01 MH51435)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- pain detection, facial action units, facial expression analysis, active appearance models, head movement, shoulder injuries, video-based pain assessment, objective pain assessment
- MeSH terms
- Artificial Intelligence, Face, Humans, Image Interpretation, Computer-Assisted, Pain, Pain Measurement, Pattern Recognition, Automated, Photography, Video Recording
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