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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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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.
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