Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering interval-valued proximity data using belief functions
Pattern Recognition Letters
Active and Dynamic Information Fusion for Facial Expression Understanding from Image Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parametric models for facial features segmentation
Signal Processing
Spontaneous vs. posed facial behavior: automatic analysis of brow actions
Proceedings of the 8th international conference on Multimodal interfaces
Predicting student emotions in computer-human tutoring dialogues
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Pairwise classifier combination using belief functions
Pattern Recognition Letters
Automatic prediction of frustration
International Journal of Human-Computer Studies
Pruning belief decision tree methods in averaging and conjunctive approaches
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
The painful face: pain expression recognition using active appearance models
Proceedings of the 9th international conference on Multimodal interfaces
Faces of pain: automated measurement of spontaneousallfacial expressions of genuine and posed pain
Proceedings of the 9th international conference on Multimodal interfaces
Pattern Recognition and Information Fusion Using Belief Functions: Some Recent Developments
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine recognition and representation of neonatal facial displays of acute pain
Artificial Intelligence in Medicine
A generative framework for real time object detection and classification
Computer Vision and Image Understanding - Special issue on eye detection and tracking
Decision making in the TBM: the necessity of the pignistic transformation
International Journal of Approximate Reasoning
Facial expression recognition based on the belief theory: comparison with different classifiers
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Classification Using Belief Functions: Relationship Between Case-Based and Model-Based Approaches
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A probabilistic framework for modeling and real-time monitoring human fatigue
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Circuits and Systems for Video Technology
Automatic detection of pain intensity
Proceedings of the 14th ACM international conference on Multimodal interaction
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The current paper presents an automatic and context sensitive system for the dynamic recognition of pain expression among the six basic facial expressions and neutral on acted and spontaneous sequences. A machine learning approach based on the Transferable Belief Model, successfully used previously to categorize the six basic facial expressions in static images [2,61], is extended in the current paper for the automatic and dynamic recognition of pain expression from video sequences in a hospital context application. The originality of the proposed method is the use of the dynamic information for the recognition of pain expression and the combination of different sensors, permanent facial features behavior, transient features behavior, and the context of the study, using the same fusion model. Experimental results, on 2-alternative forced choices and, for the first time, on 8-alternative forced choices (i.e. pain expression is classified among seven other facial expressions), show good classification rates even in the case of spontaneous pain sequences. The mean classification rates on acted and spontaneous data reach 81.2% and 84.5% for the 2-alternative and 8-alternative forced choices, respectively. Moreover, the system performances compare favorably to the human observer rates (76%), and lead to the same doubt states in the case of blend expressions.