IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Active Appearance Models Revisited
International Journal of Computer Vision
Recognizing Facial Expression: Machine Learning and Application to Spontaneous Behavior
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Fully Automatic Facial Action Recognition in Spontaneous Behavior
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Spontaneous vs. posed facial behavior: automatic analysis of brow actions
Proceedings of the 8th international conference on Multimodal interfaces
Human computing and machine understanding of human behavior: a survey
Proceedings of the 8th international conference on Multimodal interfaces
2D vs. 3D Deformable Face Models: Representational Power, Construction, and Real-Time Fitting
International Journal of Computer Vision
Image and Vision Computing
The painful face - Pain expression recognition using active appearance models
Image and Vision Computing
Probabilistic semantic classifier
SMO'09 Proceedings of the 9th WSEAS international conference on Simulation, modelling and optimization
Pain monitoring: A dynamic and context-sensitive system
Pattern Recognition
Spontaneous pain expression recognition in video sequences
VoCS'08 Proceedings of the 2008 international conference on Visions of Computer Science: BCS International Academic Conference
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Pain is typically assessed by patient self-report. Self-reported pain, however, is difficult to interpret and may be impaired or not even possible, as in young children or the severely ill. Behavioral scientists have identified reliable and valid facial indicators of pain. Until now they required manual measurement by highly skilled observers. We developed an approach that automatically recognizes acute pain. Adult patients with rotator cuff injury were video-recorded while a physiotherapist manipulated their affected and unaffected shoulder. Skilled observers rated pain expression from the video on a 5-point Likert-type scale. From these ratings, sequences were categorized as no-pain (rating of 0), pain (rating of 3, 4, or 5), and indeterminate (rating of 1 or 2). We explored machine learning approaches for pain-no pain classification. Active Appearance Models (AAM) were used to decouple shape and appearance parameters from the digitized face images. Support vector machines (SVM) were used with several representations from the AAM. Using a leave-one-out procedure, we achieved an equal error rate of 19% (hit rate = 81%) using canonical appearance and shape features. These findings suggest the feasibility of automatic pain detection from video.