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Emotion Recognition and Understanding for Emotional Human-Robot Interaction Systems

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Cover of 'Emotion Recognition and Understanding for Emotional Human-Robot Interaction Systems'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 Multi-modal Emotion Feature Extraction
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    Chapter 3 Deep Sparse Autoencoder Network for Facial Emotion Recognition
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    Chapter 4 AdaBoost-KNN with Direct Optimization for Dynamic Emotion Recognition
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    Chapter 5 Weight-Adapted Convolution Neural Network for Facial Expression Recognition
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    Chapter 6 Two-Layer Fuzzy Multiple Random Forest for Speech Emotion Recognition
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    Chapter 7 Two-Stage Fuzzy Fusion Based-Convolution Neural Network for Dynamic Emotion Recognition
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    Chapter 8 Multi-support Vector Machine Based Dempster-Shafer Theory for Gesture Intention Understanding
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    Chapter 9 Three-Layer Weighted Fuzzy Support Vector Regressions for Emotional Intention Understanding
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    Chapter 10 Dynamic Emotion Understanding Based on Two-Layer Fuzzy Support Vector Regression-Takagi-Sugeno Model
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    Chapter 11 Emotion-Age-Gender-Nationality Based Intention Understanding Using Two-Layer Fuzzy Support Vector Regression
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    Chapter 12 Emotional Human-Robot Interaction Systems
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    Chapter 13 Experiments and Applications of Emotional Human-Robot Interaction Systems
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    Chapter 14 Correction to: Dynamic Emotion Understanding Based on Two-Layer Fuzzy Support Vector Regression-Takagi-Sugeno Model
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Title
Emotion Recognition and Understanding for Emotional Human-Robot Interaction Systems
Published by
Springer International Publishing, November 2020
DOI 10.1007/978-3-030-61577-2
ISBNs
978-3-03-061576-5, 978-3-03-061577-2
Authors

Chen, Luefeng, Wu, Min, Pedrycz, Witold, Hirota, Kaoru

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