Automatically detecting pain using facial actions
- 1 September 2009
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 2009, 1-8
- https://doi.org/10.1109/acii.2009.5349321
Abstract
Pain is generally measured by patient self-report, normally via verbal communication. However, if the patient is a child or has limited ability to communicate (i.e. the mute, mentally impaired, or patients having assisted breathing) self-report may not be a viable measurement. In addition, these self-report measures only relate to the maximum pain level experienced during a sequence so a frame-by-frame measure is currently not obtainable. Using image data from patients with rotator-cuff injuries, in this paper we describe an AAM-based automatic system which can detect pain on a frame-by-frame level. We do this two ways: directly (straight from the facial features); and indirectly (through the fusion of individual AU detectors). From our results, we show that the latter method achieves the optimal results as most discriminant features from each AU detector (i.e. shape or appearance) are used.Keywords
This publication has 14 references indexed in Scilit:
- The painful face – Pain expression recognition using active appearance modelsImage and Vision Computing, 2009
- The structure, reliability and validity of pain expression: Evidence from patients with shoulder painPain, 2008
- Faces of painPublished by Association for Computing Machinery (ACM) ,2007
- The painful facePublished by Association for Computing Machinery (ACM) ,2007
- Investigating Spontaneous Facial Action Recognition through AAM Representations of the FacePublished by IntechOpen ,2007
- Application-independent evaluation of speaker detectionComputer Speech & Language, 2006
- Facial Expression AnalysisPublished by Springer Science and Business Media LLC ,2005
- Active Appearance Models RevisitedInternational Journal of Computer Vision, 2004
- Active appearance modelsIeee Transactions On Pattern Analysis and Machine Intelligence, 2001
- The consistency of facial expressions of pain: a comparison across modalitiesPain, 1992