Emotion recognition : a pattern analysis approach / edited by Amit Konar, Aruna Chakraborty.

Format:

Book

Author/Creator:

Konar, Amit.

Contributor:

Konar, Amit, editor.

Cakrabarttī, Aruṇā, editor.

Language:

English

Subjects (All):

Human-computer interaction.

Artificial intelligence.

Emotions--Computer simulation.

Emotions.

Pattern recognition systems.

Context-aware computing.

Physical Description:

1 online resource (548 p.)

Edition:

1st ed.

Place of Publication:

Hoboken, New Jersey : John Wiley & Sons, Inc., 2015.

Language Note:

English

Summary:

"Written by leaders in the field, this book provides a thorough and insightful presentation of the research methodology on emotion recognition in a highly comprehensive writing style. Topics covered include emotional feature extraction, facial recognition, human-computer interface design, neuro-fuzzy techniques, support vector machine (SVM), reinforcement learning, principal component analysis, the hidden Markov model, and probabilistic models. The result is a innovative edited volume on this timely topic for computer science and electrical engineering students and professionals"-- Provided by publisher.

Contents:

Intro

Emotion Recognition

Contents

Preface

Acknowledgments

Contributors

1 Introduction to Emotion Recognition

1.1 Basics of Pattern Recognition

1.2 Emotion Detection as a Pattern Recognition Problem

1.3 Feature Extraction

1.3.1 Facial Expression-Based Features

1.3.2 Voice Features

1.3.3 EEG Features Used for Emotion Recognition

1.3.4 Gesture- and Posture-Based Emotional Features

1.3.5 Multimodal Features

1.4 Feature Reduction Techniques

1.4.1 Principal Component Analysis

1.4.2 Independent Component Analysis

1.4.3 Evolutionary Approach to Nonlinear Feature Reduction

1.5 Emotion Classification

1.5.1 Neural Classifier

1.5.2 Fuzzy Classifiers

1.5.3 Hidden Markov Model Based Classifiers

1.5.4 k-Nearest Neighbor Algorithm

1.5.5 Naïve Bayes Classifier

1.6 Multimodal Emotion Recognition

1.7 Stimulus Generation for Emotion Arousal

1.8 Validation Techniques

1.8.1 Performance Metrics for Emotion Classification

1.9 Summary

References

Author Biographies

2 Exploiting Dynamic Dependencies Among Action Units for Spontaneous Facial Action Recognition

2.1 Introduction

2.2 Related Work

2.3 Modeling the Semantic and Dynamic Relationships Among AUs With a DBN

2.3.1 A DBN for Modeling Dynamic Dependencies among AUs

2.3.2 Constructing the Initial DBN

2.3.3 Learning DBN Model

2.3.4 AU Recognition Through DBN Inference

2.4 EXPERIMENTAL RESULTS

2.4.1 Facial Action Unit Databases

2.4.2 Evaluation on Cohn and Kanade Database

2.4.3 Evaluation on Spontaneous Facial Expression Database

2.5 Conclusion

3 Facial Expressions: A Cross-Cultural Study

3.1 Introduction

3.2 Extraction of Facial Regions and Ekman's Action Units

3.2.1 Computation of Optical Flow Vector Representing Muscle Movement.

3.2.2 Computation of Region of Interest

3.2.3 Computation of Feature Vectors Within ROI

3.2.4 Facial Deformation and Ekman's Action Units

3.3 Cultural Variation in Occurrence of Different Aus

3.4 Classification Performance Considering Cultural Variability

3.5 Conclusion

4 A Subject-dependent Facial Expression Recognition System

4.1 Introduction

4.2 Proposed Method

4.2.1 Face Detection

4.2.2 Preprocessing

4.2.3 Facial Feature Extraction

4.2.4 Face Recognition

4.2.5 Facial Expression Recognition

4.3 Experiment Result

4.3.1 Parameter Determination of the RBFNN

4.3.2 Comparison of Facial Features

4.3.3 Comparison of Face Recognition Using "Inner Face" and Full Face

4.3.4 Comparison of Subject-Dependent and Subject-Independent Facial Expression Recognition Systems

4.3.5 Comparison with Other Approaches

4.4 Conclusion

Acknowledgment

5 Facial Expression Recognition Using Independent Component Features and Hidden Markov Model

5.1 Introduction

5.2 Methodology

5.2.1 Expression Image Preprocessing

5.2.2 Feature Extraction

5.2.3 Codebook and Code Generation

5.2.4 Expression Modeling and Training Using HMM

5.3 Experimental Results

5.4 Conclusion

6 Feature Selection for Facial Expression based on Rough Set Theory

6.1 Introduction

6.2 Feature Selection for Emotion Recognition Based on Rough Set Theory

6.2.1 Basic Concepts of Rough Set Theory

6.2.2 Feature Selection Based on Rough Set and Domain-Oriented Data-Driven Data Mining Theories

6.2.3 Attribute Reduction for Emotion Recognition

6.3 Experiment Results and Discussion

6.3.1 Experiment Condition

6.3.2 Experiments for Feature Selection Method for Emotion Recognition.

6.3.3 Experiments for the Features Concerning Mouth for Emotion Recognition

6.4 Conclusion

7 Emotion Recognition from Facial Expressions Using Type-2 Fuzzy Sets

7.1 Introduction

7.2 Preliminaries on Type-2 Fuzzy Sets

7.2.1 Type-2 Fuzzy Sets

7.3 Uncertainty Management in Fuzzy-Space for Emotion Recognition

7.3.1 Principles Used in the IT2FS Approach

7.3.2 Principles Used in the GT2FS Approach

7.3.3 Methodology

7.4 Fuzzy Type-2 Membership Evaluation

7.5 Experimental Details

7.5.1 Feature Extraction

7.5.2 Creating the Type-2 Fuzzy Face-Space

7.5.3 Emotion Recognition of an Unknown Facial Expression

7.6 Performance Analysis

7.6.1 The McNemar's Test

7.6.2 Friedman Test

7.6.3 The Confusion Matrix-Based RMS Error

7.7 Conclusion

8 Emotion Recognition from Non-frontal Facial Images

8.1 Introduction

8.2 A Brief Review of Automatic Emotional Expression Recognition

8.2.1 Framework of Automatic Facial Emotion Recognition System

8.2.2 Extraction of Geometric Features

8.2.3 Extraction of Appearance Features

8.3 Databases for Non-Frontal Facial Emotion Recognition

8.3.1 BU-3DFE Database

8.3.2 BU-4DFE Database

8.3.3 CMU Multi-PIE Database

8.3.4 Bosphorus 3D Database

8.4 Recent Advances of Emotion Recognition from Non-Frontal Facial Images

8.4.1 Emotion Recognition from 3D Facial Models

8.4.2 Emotion Recognition from Non-frontal 2D Facial Images

8.5 Discussions and Conclusions

9 Maximum a Posteriori based Fusion Method for Speech Emotion Recognition

9.1 Introduction

9.2 Acoustic Feature Extraction for Emotion Recognition

9.3 Proposed Map-Based Fusion Method

9.3.1 Base Classifiers

9.3.2 MAP-Based Fusion.

9.3.3 Addressing Small Training Dataset Problem-Calculation of fc|CL(cr)

9.3.4 Training and Testing Procedure

9.4 Experiment

9.4.1 Database

9.4.2 Experiment Description

9.4.3 Results and Discussion

9.5 Conclusion

10 Emotion Recognition in Naturalistic Speech and Language-A Survey

10.1 Introduction

10.2 Tasks and Applications

10.2.1 Use-Cases for Automatic Emotion Recognition from Speech and Language

10.2.2 Databases

10.2.3 Modeling and Annotation: Categories versus Dimensions

10.2.4 Unit of Analysis

10.3 Implementation and Evaluation

10.3.1 Feature Extraction

10.3.2 Feature and Instance Selection

10.3.3 Classification and Learning

10.3.4 Partitioning and Evaluation

10.3.5 Research Toolkits and Open-Source Software

10.4 Challenges

10.4.1 Non-prototypicality, Reliability, and Class Sparsity

10.4.2 Generalization

10.4.3 Real-Time Processing

10.4.4 Acoustic Environments: Noise and Reverberation

10.5 Conclusion and Outlook

11 EEG-Based Emotion Recognition Using Advanced Signal Processing Techniques

11.1 Introduction

11.2 Brain Activity and Emotions

11.3 EEG-ER Systems: An Overview

11.4 Emotion Elicitation

11.4.1 Discrete Emotions

11.4.2 Affective States

11.4.3 Datasets

11.5 Advanced Signal Processing in EEG-ER

11.5.1 Discrete Emotions

11.5.2 Affective States

11.6 Concluding Remarks and Future Directions

12 Frequency Band Localization on Multiple Physiological Signals for Human Emotion Classification Using DWT

12.1 Introduction

12.2 Related Work

12.3 Research Methodology

12.3.1 Physiological Signals Acquisition

12.3.2 Preprocessing and Normalization

12.3.3 Feature Extraction

12.3.4 Emotion Classification.

12.4 Experimental Results and Discussions

12.5 Conclusion

12.6 Future Work

Author Biography

13 Toward Affective Brain-Computer Interface: Fundamentals and Analysis of EEG-based Emotion Classification

13.1 Introduction

13.1.1 Brain-Computer Interface

13.1.2 EEG Dynamics Associated with Emotion

13.1.3 Current Research in EEG-Based Emotion Classification

13.1.4 Addressed Issues

13.2 Materials and Methods

13.2.1 EEG Dataset

13.2.2 EEG Feature Extraction

13.2.3 EEG Feature Selection

13.2.4 EEG Feature Classification

13.3 Results and Discussion

13.3.1 Superiority of Differential Power Asymmetry

13.3.2 Gender Independence in Differential Power Asymmetry

13.3.3 Channel Reduction from Differential Power Asymmetry

13.3.4 Generalization of Differential Power Asymmetry

13.4 Conclusion

13.5 Issues and Challenges Toward ABCIs

13.5.1 Directions for Improving Estimation Performance

13.5.2 Online System Implementation

14 Bodily Expression for Automatic Affect Recognition

14.1 Introduction

14.2 Background and Related Work

14.2.1 Body as an Autonomous Channel for Affect Perception and Analysis

14.2.2 Body as an Additional Channel for Affect Perception and Analysis

14.2.3 Bodily Expression Data and Annotation

14.3 Creating a Database of Facial and Bodily Expressions: The Fabo Database

14.4 Automatic Recognition of Affect from Bodily Expressions

14.4.1 Body as an Autonomous Channel for Affect Analysis

14.4.2 Body as an Additional Channel for Affect Analysis

14.5 Automatic Recognition of Bodily Expression Temporal Dynamics

14.5.1 Feature Extraction

14.5.2 Feature Representation and Combination

14.5.3 Experiments

14.6 Discussion and Outlook

14.7 Conclusions.

Acknowledgments.

Notes:

Bibliographic Level Mode of Issuance: Monograph

Includes bibliographical references and index.

Description based on print version record.

ISBN:

9781118910603

1118910605

9781118910566

1118910567

9781118910610

1118910613

OCLC:

898424087