Active shape models—their training and application
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Active Appearance Models Revisited
International Journal of Computer Vision
Fully Automatic Facial Action Recognition in Spontaneous Behavior
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Faces of pain: automated measurement of spontaneousallfacial expressions of genuine and posed pain
Proceedings of the 9th international conference on Multimodal interfaces
Model of Frequency Analysis in the Visual Cortex and the Shape from Texture Problem
International Journal of Computer Vision
The painful face - Pain expression recognition using active appearance models
Image and Vision Computing
Registration Invariant Representations for Expression Detection
DICTA '10 Proceedings of the 2010 International Conference on Digital Image Computing: Techniques and Applications
Pain monitoring: A dynamic and context-sensitive system
Pattern Recognition
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Previous efforts suggest that occurrence of pain can be detected from the face. Can intensity of pain be detected as well? The Prkachin and Solomon Pain Intensity (PSPI) metric was used to classify four levels of pain intensity (none, trace, weak, and strong) in 25 participants with previous shoulder injury (McMaster-UNBC Pain Archive). Participants were recorded while they completed a series of movements of their affected and unaffected shoulders. From the video recordings, canonical normalized appearance of the face (CAPP) was extracted using active appearance modeling. To control for variation in face size, all CAPP were rescaled to 96x96 pixels. CAPP then was passed through a set of Log-Normal filters consisting of 7 frequencies and 15 orientations to extract 9216 features. To detect pain level, 4 support vector machines (SVMs) were separately trained for the automatic measurement of pain intensity on a frame-by-frame level using both 5-folds cross-validation and leave-one-subject-out cross-validation. F1 for each level of pain intensity ranged from 91% to 96% and from 40% to 67% for 5-folds and leave-one-subject-out cross-validation, respectively. Intra-class correlation, which assesses the consistency of continuous pain intensity between manual and automatic PSPI was 0.85 and 0.55 for 5-folds and leave-one-subject-out cross-validation, respectively, which suggests moderate to high consistency. These findings show that pain intensity can be reliably measured from facial expression in participants with orthopedic injury.