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Table of Contents
Introduction
Multi-modal emotion feature extraction
Deep sparse autoencoder network for facial emotion recognition
AdaBoost-knn with direct optimization for dynamic emotion recognition
Weight-adapted convolution neural network for facial expression recognition
Two-layer fuzzy multiple random forest for speech emotion recognition
Two-stage fuzzy fusion based-convolution neural network for dynamic emotion recognition
Multi-support vector machine based Dempster-Shafer theory for gesture intention understanding
Three-layer weighted fuzzy support vector regressions for emotional intention understanding
Dynamic emotion understanding based on two-layer fuzzy fuzzy support vector regression-Takagi-Sugeno model
Emotion-age-gender-nationality based intention understanding using two-layer fuzzy support vector regression
Emotional human-robot interaction systems
Experiments and applications of emotional human-robot.
Multi-modal emotion feature extraction
Deep sparse autoencoder network for facial emotion recognition
AdaBoost-knn with direct optimization for dynamic emotion recognition
Weight-adapted convolution neural network for facial expression recognition
Two-layer fuzzy multiple random forest for speech emotion recognition
Two-stage fuzzy fusion based-convolution neural network for dynamic emotion recognition
Multi-support vector machine based Dempster-Shafer theory for gesture intention understanding
Three-layer weighted fuzzy support vector regressions for emotional intention understanding
Dynamic emotion understanding based on two-layer fuzzy fuzzy support vector regression-Takagi-Sugeno model
Emotion-age-gender-nationality based intention understanding using two-layer fuzzy support vector regression
Emotional human-robot interaction systems
Experiments and applications of emotional human-robot.