Title :
Pain recognition and intensity rating based on Comparative Learning
Author :
Werner, Philipp ; Al-Hamadi, Ayoub ; Niese, Robert
Author_Institution :
Inst. for Electron., Signal Process. & Commun. (IESK, Otto-von-Guericke-Univ., Magdeburg, Germany
fDate :
Sept. 30 2012-Oct. 3 2012
Abstract :
Automatic pain recognition can improve medical treatment, especially when the patient is not able to utter on his pain experience. Facial expressions with their intensities and dynamics contain valuable information for recognising pain. We propose a concept for distinguishing facial expressions of pain from others and assessing the pain expression intensity. It is based on a Support Vector Machine (SVM) classifier and a function model for intensity rating. The intensity model is trained using Comparative Learning, a new technique that simplifies labelling of the data. Using a database of 3D posed pain sequences we show the suitability of the concept to recognise pain expressions, distinguish different intensities and spot even slight intensity changes in its temporal context.
Keywords :
emotion recognition; face recognition; image classification; learning (artificial intelligence); support vector machines; 3D posed pain sequences; SVM classifier; automatic pain recognition; comparative learning; facial expressions; intensity rating; medical treatment; support vector machine classifier; Databases; Face recognition; Feature extraction; Labeling; Pain; Support vector machines; Vectors; Facial Expression Recognition; Intensity Rating; Pain Recognition;
Conference_Titel :
Image Processing (ICIP), 2012 19th IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
978-1-4673-2534-9
Electronic_ISBN :
1522-4880
DOI :
10.1109/ICIP.2012.6467359