Coding, Analysis, Interpretation, and Recognition of Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Lower Face Action Units for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
An Expert System for Multiple Emotional Classification of Facial Expressions
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
To feel or not to feel: the role of affect in human-computer interaction
International Journal of Human-Computer Studies - Application of affective computing in humanComputer interaction
Authentic facial expression analysis
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Comparison of Classification Methods for Detecting Emotion from Mandarin Speech
IEICE - Transactions on Information and Systems
Emotional states in judicial courtrooms: An experimental investigation
Speech Communication
Pattern Recognition Letters
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Emotion is fundamental to human experience influencing cognition, perception and everyday tasks such as learning, communication and even rational decision-making. This aspect must be considered in human-computer interaction. In this paper, we compare four different weighting functions in weighted KNN-based classifiers to recognize five emotions, including anger, happiness, sadness, neutral and boredom, from Mandarin emotional speech. The classifiers studied include weighted KNN, weighted CAP, and weighted DKNN. To give a baseline performance measure, we also adopt traditional KNN classifier. The experimental results show that the used Fibonacci weighting function outperforms than others in all weighted classifiers. The highest accuracy achieves 81.4% with weighted D-KNN classifier.