内容简介：　　　

   This book focuses on the key technologies and scientific problems involved in emotional robot systems, such as multimodal emotion recognition (i.e., facial expression/speech/gesture and their multimodal emotion recognition) and emotion intention understanding, and presents the design and application examples of emotional human-robot interaction (HRI) systems. Aiming at the development needs of emotional robots and emotional HRI systems, this book introduces basic concepts, system architecture, and system functions of affective computing and emotional robot systems. With the professionalism of this book, it serves as a useful reference for engineers in affective computing, and graduate students interested in emotion recognition and intention understanding. This book offers the latest approaches to this active research area. It provides readers with the state-of-the-art methods of multimodal emotion recognition, intention understanding, and application examples of emotional HRI systems.

英文目录：
1 Introduction
　　1.1 Emotion Feature Extraction and Recognition
　　1.2 Emotion Understanding
　　1.3 Emotional Human-Robot Interaction System
　　1.4 Organization of This Book
　　References
2 Multi-modal Emotion Feature Extraction
　　2.1 Introduction
　　2.2 Facial Expression Feature Extraction
　　2.3 Speech Emotion Feature Extraction
　　2.4 Gesture Feature Extraction
　　2.5 Summary
　　References
3 Deep Sparse Autoencoder Network for Facial Emotion Recognition
　　3.1 Introduction
　　3.2 Softmax Regression Based Deep Sparse Autoencoder Network
　　3.3 ROI Based Face Image Preprocessing
　　3.4 Expand the Encode and Decode Network
　　3.5 Softmax Regression
　　3.6 Overall Weight Training
　　3.7 Experiments
　　　　3.7.1 Fine-Tune Effect on Performance of Recognition
　　　　3.7.2 The Number of Hidden Layer Node’s Effect on Performance of Recognition
　　　　3.7.3 Recognition Rate
　　3.8 Summary
　　References
4 AdaBoost-KNN with Direct Optimization for Dynamic Emotion Recognition
　　4.1 Introduction
　　4.2 Dynamic Feature Extraction Using Candide-3 Model
　　4.3 Adaptive Feature Selection Based on Plus-L Minus-R Selection
　　4.4 AdaBoost-KNN Based Emotion Recognition
　　4.5 AdaBoost-KNN with Direct Optimization for Emotion Recognition
　　4.6 Experiments
　　　　4.6.1 Experimental Environment and Data Selection
　　　　4.6.2 Simulations and Analysis
　　　　4.6.3 Preliminary Application Experiments on Emotional Social Robot System
　　4.7 Summary
　　References
5 Weight-Adapted Convolution Neural Network for Facial Expression Recognition
　　5.1 Introduction
　　5.2 Facial Expression Image Preprocessing
　　5.3 Principal Component Analysis for Extracting Expression Feature
　　5.4 Weight-Adapted Convolution Neural Network for Recognizing Expression Feature
　　　　5.4.1 Feature Learning Based on Deep Convolution Neural Network
　　　　5.4.2 Softmax Regression for Feature Recognition
　　5.5 Hybrid Genetic Algorithm for Optimizing Weight Adaptively
　　5.6 Experiments
　　5.7 Summary
　　References
6 Two-Layer Fuzzy Multiple Random Forest for Speech Emotion Recognition
　　6.1 Introduction
　　6.2 Feature Extraction
　　6.3 Fuzzy-c-Means Based Features Classification
　　6.4 Two-Layer Fuzzy Multiple Random Forest
　　6.5 Experiments
　　　　6.5.1 Data Setting
　　　　6.5.2 Environment Setting
　　　　6.5.3 Simulations and Analysis
　　6.6 Summary
　　References
7 Two-Stage Fuzzy Fusion Based-Convolution Neural Network for Dynamic Emotion Recognition
　　7.1 Introduction
　　7.2 Dynamic Emotion Feature Extraction
　　7.3 Deep Convolution Neural Network for Extracting High-Level Emotion Semantic Features
　　7.4 Feature Fusion Based on Canonical Correlation Analysis
　　7.5 Decision Fusion Based on Fuzzy Broad Learning System
　　7.6 Two-Stage Fuzzy Fusion Strategy
　　7.7 Experiments
　　　　7.7.1 Data Setting
　　　　7.7.2 Experiments for Hyperparameters
　　　　7.7.3 Experimental Results and Analysis
　　7.8 Summary
　　References
8 Multi-support Vector Machine Based Dempster-Shafer Theory for Gesture Intention Understanding
　　8.1 Introduction
　　8.2 Foreground Segmentation and Feature Extraction
　　　　8.2.1 Foreground Segmentation Based on Depth and RGB Images
　　　　8.2.2 Speeded-Up Robust Features Based Gesture Feature Extraction
　　8.3 Encoding Speeded-Up Robust Features: Sparse Coding
　　8.4 Multi-class Linear Support Vector Machines
　　8.5 Dempster-Shafer Evidence Theory for Decision-Level Fusion
　　8.6 Experiments
　　　　8.6.1 Experimental Setting
　　　　8.6.2 Experimental Environment and Setup
　　　　8.6.3 Experimental Results and Analysis
　　8.7 Summary
　　References
9 Three-Layer Weighted Fuzzy Support Vector Regressions for Emotional Intention Understanding
　　9.1 Introduction
　　9.2 Support Vector Regression
　　9.3 Three-Layer Fuzzy Support Vector Regression
　　9.4 Characteristics Analysis of Emotional Intention Understanding
　　9.5 Three-Layer Fuzzy Support Vector Regression-Based Intention Understanding Model
　　9.6 Experiments
　　　　9.6.1 Experiment Setting
　　　　9.6.2 Experiments on Three-Layer Fuzzy Support Vector Regression Based Intention Understanding Model
　　9.7 Summary
　　References
10 Dynamic Emotion Understanding Based on Two-Layer Fuzzy Support Vector Regression-Takagi-Sugeno Model
　　10.1 Introduction
　　10.2 Dynamic Emotion Recognition Using Candide3-Based Feature Point Matching
　　10.3 Two-Layer Fuzzy Support Vector Regression for Emotional Intention Understanding
　　10.4 Two-Layer Fuzzy Support Vector Regression Takagi-Sugeno Model for Emotional Intention Understanding
　　10.5 Experiments
　　　　10.5.1 Experimental Environment
　　　　10.5.2 Self-Built Data
　　　　10.5.3 Experiments on Dynamic Emotion Recognition and Understanding
　　10.6 Summary
　　References
11 Emotion-Age-Gender-Nationality Based Intention Understanding Using Two-Layer Fuzzy Support Vector Regression
　　11.1 Introduction
　　11.2 Two-Layer Fuzzy Support Vector Regression
　　　　11.2.1 Support Vector Regression
　　　　11.2.2 Two-Layer Fuzzy Support Vector Regression
　　11.3 Intention Understanding
　　　　11.3.1 Emotion Based Intention Understanding
　　　　11.3.2 Characteristics Analysis
　　11.4 Intention Understanding Model
　　　　11.4.1 Emotion Recognition
　　　　11.4.2 Questionnaire
　　　　11.4.3 ID Mapping
　　11.5 Two-Layer Fuzzy Support Vector Regression Based Intention Understanding
　　　　11.5.1 Intention Generation by Fuzzy Inference
　　11.6 Memory Retrieval for Intention Understanding
　　11.7 Experiments
　　　　11.7.1 Experiment Setting
　　　　11.7.2 Experiments on Two-Layer Fuzzy Support Vector Regression Based Intention Understanding Model
　　11.8 Summary
　　References
12 Emotional Human-Robot Interaction Systems
　　12.1 Introduction
　　12.2 Basic Emotional Human-Robot Interaction Systems
　　12.3 Design of Emotional Human-Robot Interaction System
　　12.4 Summary
　　References
13 Experiments and Applications of Emotional Human-Robot Interaction Systems
　　13.1 Introduction
　　13.2 Emotional Interaction Scenario Setting
　　13.3 Multi-modal Emotion Recognition Experiments Based on Facial Expression, Speech and Gesture
　　13.4 Emotional Intention Understanding Experiments Based on Emotion, Age, Gender, and Nationality
　　13.5 Application of Multi-modal Emotional Intention Understanding
　　　　13.5.1 Self-built Data
　　　　13.5.2 Experiments on Dynamic Emotion Recognition and Understanding
　　13.6 Summary
　　References
Index