Efficient Region Tracking With Parametric Models of Geometry and Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Detection in Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
The production and recognition of emotions in speech: features and algorithms
International Journal of Human-Computer Studies - Application of affective computing in human—Computer interaction
Affective multimodal human-computer interaction
Proceedings of the 13th annual ACM international conference on Multimedia
The eNTERFACE'05 Audio-Visual Emotion Database
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
2005 Special Issue: Emotion recognition in human-computer interaction
Neural Networks - Special issue: Emotion and brain
A note on Platt's probabilistic outputs for support vector machines
Machine Learning
Information combination operators for data fusion: a comparative review with classification
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Multimodal information fusion application to human emotion recognition from face and speech
Multimedia Tools and Applications
Affective speaker state analysis in the presence of reverberation
International Journal of Speech Technology
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Modern HCI systems tend to build a new channel of human-computer interaction in which the human emotion is understandable by computers. Since Humans depict emotional behavior in combination of various modalities (e.g. facial expression, speech articulations); The HCI should reliably perceive emotional information from multiple channels. The goal of the paper is to propose an approach for combining emotion related information from speech and facial expression. Two fusion approaches (feature level and decision level) are presented and compared with experimental results. Results show that the decision level fusion performs better than the other systems.