Emotion Recognition E-Book

Amit Konar

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 1088

Veröffentlichungsjahr: 2014

Beschreibung

A timely book containing foundations and current research directions on emotion recognition by facial expression, voice, gesture and biopotential signals

This book provides a comprehensive examination of the research methodology of different modalities of emotion recognition. Key topics of discussion include facial expression, voice and biopotential signal-based emotion recognition. Special emphasis is given to feature selection, feature reduction, classifier design and multi-modal fusion to improve performance of emotion-classifiers.

Written by several experts, the book includes several tools and techniques, including dynamic Bayesian networks, neural nets, hidden Markov model, rough sets, type-2 fuzzy sets, support vector machines and their applications in emotion recognition by different modalities. The book ends with a discussion on emotion recognition in automotive fields to determine stress and anger of the drivers, responsible for degradation of their performance and driving-ability.

There is an increasing demand of emotion recognition in diverse fields, including psycho-therapy, bio-medicine and security in government, public and private agencies. The importance of emotion recognition has been given priority by industries including Hewlett Packard in the design and development of the next generation human-computer interface (HCI) systems.

Emotion Recognition: A Pattern Analysis Approach would be of great interest to researchers, graduate students and practitioners, as the book

• Offers both foundations and advances on emotion recognition in a single volume
• Provides a thorough and insightful introduction to the subject by utilizing computational tools of diverse domains
• Inspires young researchers to prepare themselves for their own research
• Demonstrates direction of future research through new technologies, such as Microsoft Kinect, EEG systems etc.

Leseprobe

EMOTION RECOGNITION

A Pattern Analysis Approach

Edited by

AMIT KONAR

Artificial Intelligence Laboratory Department of Electronics and Telecommunication Engineering Jadavpur University Kolkata, India

ARUNA CHAKRABORTY

Department of Computer Science & Engineering St. Thomas' College of Engineering & Technology Kolkata, India

Copyright © 2015 by John Wiley & Sons, Inc. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada.

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Library of Congress Cataloging-in-Publication Data:Konar, Amit. Emotion recognition : a pattern analysis approach / Amit Konar, Aruna Chakraborty. pages cm Includes index. ISBN 978-1-118-13066-7 (hardback) 1. Human-computer interaction. 2. Artificial intelligence. 3. Emotions–Computer simulation. 4. Pattern recognition systems. 5. Context-aware computing. I. Chakraborty, Aruna, 1977- II. Title. QA76.9.H85K655 2014 004.01'9–dc23

2014024314

To our parents

CONTENTS

Preface

Acknowledgments

Contributors

Chapter 1: Introduction to Emotion Recognition

1.1 Basics of Pattern Recognition

1.2 Emotion Detection as a Pattern Recognition Problem

1.3 Feature Extraction

1.4 Feature Reduction Techniques

1.5 Emotion Classification

1.6 Multimodal Emotion Recognition

1.7 Stimulus Generation for Emotion Arousal

1.8 Validation Techniques

1.9 Summary

References

Author Biographies

Chapter 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

2.4 Experimental Results

2.5 Conclusion

References

Author Biographies

Note

Chapter 3: Facial Expressions: A Cross-Cultural Study

3.1 Introduction

3.2 Extraction of Facial Regions and Ekman’s Action Units

3.3 Cultural Variation in Occurrence of Different Aus

3.4 Classification Performance Considering Cultural Variability

3.5 Conclusion

References

Author Biographies

Notes

Chapter 4: A Subject-dependent Facial Expression Recognition System

4.1 Introduction

4.2 Proposed Method

4.3 Experiment Result

4.4 Conclusion

Acknowledgment

References

Author Biographies

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

5.1 Introduction

5.2 Methodology

5.3 Experimental Results

5.4 Conclusion

Acknowledgments

References

Author Biographies

Chapter 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

6.3 Experiment Results and Discussion

6.4 Conclusion

Acknowledgments

References

Author Biographies

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

7.1 Introduction

7.2 Preliminaries on Type-2 Fuzzy Sets

7.3 Uncertainty Management in Fuzzy-Space for Emotion Recognition

7.4 Fuzzy Type-2 Membership Evaluation

7.5 Experimental Details

7.6 Performance Analysis

7.7 Conclusion

References

Author Biographies

Chapter 8: Emotion Recognition from Non-frontal Facial Images

8.1 Introduction

8.2 A Brief Review of Automatic Emotional Expression Recognition

8.3 Databases for Non-Frontal Facial Emotion Recognition

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

8.5 Discussions and Conclusions

Acknowledgments

References

Author Biographies

Chapter 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

9.4 Experiment

9.5 Conclusion

References

Author Biographies

Chapter 10: Emotion Recognition in Naturalistic Speech and Language—A Survey

10.1 Introduction

10.2 Tasks and Applications

10.3 Implementation and Evaluation

10.4 Challenges

10.5 Conclusion and Outlook

Acknowledgment

References

Author Biographies

Notes

Chapter 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

11.5 Advanced Signal Processing in EEG-ER

11.6 Concluding Remarks and Future Directions

References

Author Biographies

Chapter 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

12.4 Experimental Results and Discussions

12.5 Conclusion

12.6 Future Work

Acknowledgments

References

Author Biography

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

13.1 Introduction

13.2 Materials and Methods

13.3 Results and Discussion

13.4 Conclusion

13.5 Issues and Challenges Toward ABCIs

Acknowledgments

References

Author Biographies

Chapter 14: Bodily Expression for Automatic Affect Recognition

14.1 Introduction

14.2 Background and Related Work

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

14.4 Automatic Recognition of Affect from Bodily Expressions

14.5 Automatic Recognition of Bodily Expression Temporal Dynamics

14.6 Discussion and Outlook

14.7 Conclusions

Acknowledgments

References

Author Biographies

Note

Chapter 15: Building a Robust System for Multimodal Emotion Recognition

15.1 Introduction

15.2 Related Work

15.3 The Callas Expressivity Corpus

15.4 Methodology

15.5 Multisensor Data Fusion

15.6 Experiments

15.7 Online Recognition System

15.8 Conclusion

Acknowledgment

References

Author Biographies

Notes

Chapter 16: Semantic AudioVisual Data Fusion for Automatic Emotion Recognition

16.1 Introduction

16.2 Related Work

16.3 Data Set Preparation

16.4 Architecture

16.5 Results

16.6 Conclusion

References

Author Biographies

Chapter 17: A Multilevel Fusion Approach for Audiovisual Emotion Recognition

17.1 Introduction

17.2 Motivation and Background

17.3 Facial Expression Quantification

17.4 Experiment Design

17.5 Experimental Results and Discussion

17.6 Conclusion

References

Author Biographies

Chapter 18: From A Discrete Perspective of Emotions to Continuous, Dynamic, and Multimodal Affect Sensing

18.1 Introduction

18.2 A Novel Method for Discrete Emotional Classification of Facial Images

18.3 A 2D Emotional Space for Continuous and Dynamic Facial Affect Sensing

18.4 Expansion to Multimodal Affect Sensing

18.5 Building Tools That Care

18.6 Concluding Remarks and Future Work

Acknowledgments

References

Author Biographies

Chapter 19: AudioVisual Emotion Recognition using Semi-Coupled Hidden Markov Model with State-Based Alignment Strategy

19.1 Introduction

19.2 Feature Extraction

19.3 Semi-Coupled Hidden Markov Model

19.4 Experiments

19.5 Conclusion

References

Author Biographies

Chapter 20: Emotion Recognition in Car Industry

20.1 Introduction

20.2 An Overview of Application for the Car Industry

20.3 Modality-Based Categorization

20.4 Emotion-Based Categorization

20.5 Two Exemplar Cases

20.6 Open Issues and Future Steps

20.7 Conclusion

References

Author Biographies

Notes

Index

End User License Agreement

List of Tables

Chapter 2

Table 2.1

Table 2.2

Chapter 3

Table 3.1

Table 3.2

Table 3.3

Table 3.4

Table 3.5

Table 3.6

Table 3.7

Table 3.8

Table 3.9

Table 3.10

Chapter 4

Table 4.1

Table 4.2

Table 4.3

Table 4.4

Table 4.5

Table 4.6

Table 4.7

Table 4.8

Table 4.9

Table 4.10

Chapter 5

Table 5.1

Table 5.2

Table 5.3

Table 5.4

Table 5.5

Chapter 6

Table 6.1

Table 6.2

Table 6.3

Table 6.4

Table 6.5

Chapter 7

Table 7.1

Table 7.2

Table 7.3

Table 7.4

Table 7.5

Table 7.6

Table 7.7

Table 7.8

Table 7.9

Table 7.10

Table 7.11

Chapter 8

Table 8.1

Table 8.2

Table 8.3

Table 8.4

Table 8.5

Table 8.6

Table 8.7

Chapter 9

Table 9.1

Table 9.2

Chapter 11

Table 11.1

Table 11.2

Chapter 12

Table 12.1

Table 12.2

Table 12.3

Table 12.4

Table 12.5

Table 12.6

Table 12.7

Table 12.8

Table 12.9

Table 12.10

Table 12.11

Chapter 13

Table 13.1

Table 13.2

Table 13.3

Table 13.4

Chapter 14

Table 14.1

Table 14.2

Table 14.3

Table 14.4

Chapter 15

Table 15.1

Table 15.2

Table 15.3

Chapter 16

Table 16.1

Table 16.2

Table 16.3

Table 16.4

Table 16.5

Table 16.6

Table 16.7

Table 16.8

Table 16.9

Chapter 17

Table 17.1

Table 17.2

Table 17.3

Table 17.4

Table 17.5

Table 17.6

Table 17.7

Chapter 18

Table 18.1

Table 18.2

Table 18.3

Table 18.4

Table 18.5

Chapter 19

Table 19.1

Table 19.2

Table 19.3

Table 19.4

Table 19.5

Chapter 20

Table 20.1

Table 20.2

Table 20.3

Table 20.4

Table 20.5

Table 20.6

Table 20.7

Table 20.8

Table 20.9

Table 20.10

List of Illustrations

Chapter 1

Figure 1.1 Basic steps of pattern recognition.

Figure 1.2 The international 10-20 electrode placement system.

Figure 1.3 Defining support vector for a linear SVM system.

Chapter 2

Figure 2.1 A pair of (a) static network

B

0

and (b) transition network

B

→

define the dynamic dependencies for three random variables

X

1

,

X

2

,

X

3

. (c) The corresponding “unrolled” DBN for

T

+ 1 time slices.

Figure 2.2 (a) The initial transition network and (b) the learned transition network by the proposed algorithm for AU modeling. The self-arrow at each AU node indicates the temporal evolution of a single AU from the previous time slice to the current time slice. The dashed line with arrow from at time − 1 to ( ≠ ) at time indicates the dynamic dependency between different AUs.