Affective computing
Latent semantic analysis of facial action codes for automatic facial expression recognition
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
3D Facial Expression Recognition Based on Primitive Surface Feature Distribution
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Predicting student emotions in computer-human tutoring dialogues
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Facial Action Unit Recognition by Exploiting Their Dynamic and Semantic Relationships
IEEE Transactions on Pattern Analysis and Machine Intelligence
The painful face: pain expression recognition using active appearance models
Proceedings of the 9th international conference on Multimodal interfaces
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semantic Classifier for Affective Computing
CIMCA '08 Proceedings of the 2008 International Conference on Computational Intelligence for Modelling Control & Automation
Recognition of facial expressions and measurement of levels of interest from video
IEEE Transactions on Multimedia
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive and personalized body networking
Proceedings of the Fifth International Conference on Body Area Networks
Adaptive and personalised body networking
International Journal of Autonomous and Adaptive Communications Systems
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Various methods like Linear Discriminant Analysis, k-Nearest Neighbors, Support Vector Machines or Decision Trees are used to successfully solve many classification problems. However, there is no single classifier that works the best in all classification problems. In pervasive adaptive systems the classification of human cognitive, emotional and physical states may be somewhat specific and should be closer to the way humans actually recognize these states. As an attempt in this direction we propose a probabilistic semantic classifier, which is based on discretization, structure identification and semantic optimization. Furthermore, this classifier supports three types of learning characteristic for humans: by repetition, by generalization and by specialization.