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Automated Face Analysis Emerging Technologies and Research Premier Reference Source 1st Edition by Daijin Kim, Jaewon Sung ISBN 978-1605662169 160566216X

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Automated Face Analysis Emerging Technologies and Research Premier Reference Source 1st Edition Daijin Kim Digital Instant Download

Author(s): Daijin Kim, Jaewon Sung
ISBN(s): 9781605662176, 1605662178
Edition: 1
File Details: PDF, 17.70 MB
Year: 2009
Language: english
SKU: EB-1796774 Category: Tags: ,

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Product details: 

ISBN 10: 160566216X

ISBN 13: 978-1605662169

Author: Daijin Kim, Jaewon Sung

Since research on face recognition began in the 1960’s, the field has rapidly widened to automated face analysis including face detection, facial gesture recognition, and facial expression recognition. Automated Face Analysis: Emerging Technologies and Research provides theoretical background to understand the overall configuration and challenging problem of automated face analysis systems, featuring a comprehensive review of recent research for the practical implementation of the analysis system. A must-read for practitioners and students in the field, this book provides understanding by systematically dividing the subject into several subproblems such as detection, modeling, and tracking of the face.
 

Table of contents: 

Chapter.I

Introduction

1.1 Roles of the Face in the Human Communication

1.2 Goals of the Automated Face Analysis.

1.3 References

Chapter.II

Face.and.Eye.Detection

2.1 AdaBoost

2.2 Modi.ed Census Transform.

2.3 Face Detection Using Face Certainty Map

2.4 Eye Detection Using MCT-Based Feature Correlation.

2.5 Face Disguise Discrimination using AdaBoost Learning..

2.6 Conclusion

2.7 References

Chapter.III

Face.Modeling

3.1 Active Shape Models

3.2 Active Appearance Models.

3.3 AAM with Stereo Vision..

3.4 A View-Based AAM Approach for Pose-Invariant Representation…

3.5 A Unified Gradient-Based Approach for Combining ASM into AAM….

3.6 Conclusion

3.7 References.

Chapter.IV

Face.Tracking

4.1 Particle Filters.

4.2 Geometric Head Models

4.3 Incremental Principle Component Analysis..

4.4 A Naive Approach

4.5 Background-Robust Face Tracking Using AAM and ACM.

4.6 Pose-Robust Face Tracking Using AAM and CHM..

4.7 Illumination-Robust Face Tracking Using AAM and IPCA..

4.8 Robust Face Tracking Using AAM in Particle Filter Framework..

4.9 Conclusion

4.10 References

4.11 Endnotes.

Chapter.V

Face.Recognition

5.1 Mixture Models

5.2 Embedded Hidden Markov Model

5.3 Local Feature Analysis

5.4 Tensor Analysis

5.5 3D Morphable Models.

5.6 Face Recognition Using Mixture Models

5.7 Face Recognition Using Embedded HMM.

5.8 Face Recognition Using LFA..

5.9 Face Recognition Using Tensor-Based AAM..

5.10 Face Recognition Using 3D MM.

5.11 Conclusion

5.12 References.

Chapter.VI

Facial. Expression. Recognition

6.1 Generalized Discriminant Analysis

6.2 Bilinear Models

6.3 Relative Expression Image…

6.4 Facial Expression Recognition Using AAM Features and GDA.

6.5 Natural Facial Expression Recognition Using Differential AAM and Manifold Learning

6.6 Subtle Facial Expression Recognition Using Motion Magnification….

6.7 Facial Expression Synthesis Using AAM and Bilinear Model..

6.8 Conclusion

6.9 References

Chapter.VII

Facial.Gesture.Recognition

7.1 Hidden Markov Model

7.2 Facial Gesture Recognition Using CHM and HMM.

7.3 Facial Gesture-Based TV Remote Controller

7.4 Conclusion

7.5 References.

Chapter.VIII

Human.Motion.Analysis

8.1 Scale Adaptive Filters.

8.2 Self Organizing Map.

8.3 Iterative Closest Point Algorithm

8.4 Human Detection Using Pose-Robust SAFS.

8.5 Hand Gesture Recognition for Controling the Smart Home.

8.6 Hand Gesture Recognition for Understanding Musical

Conduction Action..

8.7 2D Body Gesture Recognition Using Forward Spotting

Accumulative HMMs.

8.8 3D Body Gesture Recognition Using 3D Articulated Human Motion Body Model

8.9 Conclusion

8.10 References

811 Endnota

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Tags: Daijin Kim, Jaewon Sung, Automated Face, Analysis Emerging, Technologies and Research