Driver monitoring for a human-centered driver assistance system
Proceedings of the 1st ACM international workshop on Human-centered multimedia
A survey of affect recognition methods: audio, visual and spontaneous expressions
Proceedings of the 9th international conference on Multimodal interfaces
EURASIP Journal on Advances in Signal Processing
Dynamic Human Fatigue Detection Using Feature-Level Fusion
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
Landmark detection from mobile life log using a modular Bayesian network model
Expert Systems with Applications: An International Journal
Measuring human factors in port activities by using simulation
SpringSim '09 Proceedings of the 2009 Spring Simulation Multiconference
Detection of driver fatigue caused by sleep deprivation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Computer vision-based human body segmentation and posture estimation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special section: Best papers from the 2007 biometrics: Theory, applications, and systems (BTAS 07) conference
Gabor-based dynamic representation for human fatigue monitoring in facial image sequences
Pattern Recognition Letters
Eyes location by regional features and knowledge matching for driver fatigue detection
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
A driver fatigue recognition model based on information fusion and dynamic Bayesian network
Information Sciences: an International Journal
Dynamic features based driver fatigue detection
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on recent advances in biometrics
Spatiotemporal-boosted DCT features for head and face gesture analysis
HBU'10 Proceedings of the First international conference on Human behavior understanding
Robust classification of face and head gestures in video
Image and Vision Computing
IUKM'11 Proceedings of the 2011 international conference on Integrated uncertainty in knowledge modelling and decision making
Pain monitoring: A dynamic and context-sensitive system
Pattern Recognition
Multi-mode saliency dynamics model for analyzing gaze and attention
Proceedings of the Symposium on Eye Tracking Research and Applications
Spontaneous pain expression recognition in video sequences
VoCS'08 Proceedings of the 2008 international conference on Visions of Computer Science: BCS International Academic Conference
A dynamic multimodal approach for assessing learners' interaction experience
Proceedings of the 15th ACM on International conference on multimodal interaction
Facial expression recognition based on anatomy
Computer Vision and Image Understanding
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A probabilistic framework based on the Bayesian networks for modeling and real-time inferring human fatigue by integrating information from various sensory data and certain relevant contextual information is introduced. A static fatigue model that captures the static relationships between fatigue, significant factors that cause fatigue, and various sensory observations that typically result from fatigue is first presented. Such a model provides mathematically coherent and sound basis for systematically aggregating uncertain evidences from different sources, augmented with relevant contextual information. The static model, however, fails to capture the dynamic aspect of fatigue. Fatigue is a cognitive state that is developed over time. To account for the temporal aspect of human fatigue, the static fatigue model is extended based on dynamic Bayesian networks. The dynamic fatigue model allows to integrate fatigue evidences not only spatially but also temporally, therefore, leading to a more robust and accurate fatigue modeling and inference. A real-time nonintrusive fatigue monitor was built based on integrating the proposed fatigue model with a computer vision system developed for extracting various visual cues typically related to fatigue. Performance evaluation of the fatigue monitor using both synthetic and real data demonstrates the validity of the proposed fatigue model in both modeling and real-time inference of fatigue