Emotion Recognition and Understanding for Emotional Human-Robot Interaction Systems (Paperback)

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.

Witold Pedrycz (지은이)    정보 더보기

Kaoru Hirota (지은이)    정보 더보기

Min Wu (지은이)    정보 더보기

Luefeng Chen (지은이)    정보 더보기

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