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The Springer International Series in Engineering and Computer Science

Face Image Analysis by Unsupervised Learning

Authors: Bartlett, Marian Stewart

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eBook $119.00
price for USA in USD (gross)
  • ISBN 978-1-4615-1637-8
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Hardcover $159.99
price for USA in USD
  • ISBN 978-0-7923-7348-3
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Softcover $159.99
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  • ISBN 978-1-4613-5653-0
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About this book

Face Image Analysis by Unsupervised Learning explores adaptive approaches to image analysis. It draws upon principles of unsupervised learning and information theory to adapt processing to the immediate task environment. In contrast to more traditional approaches to image analysis in which relevant structure is determined in advance and extracted using hand-engineered techniques, Face Image Analysis by Unsupervised Learning explores methods that have roots in biological vision and/or learn about the image structure directly from the image ensemble. Particular attention is paid to unsupervised learning techniques for encoding the statistical dependencies in the image ensemble.
The first part of this volume reviews unsupervised learning, information theory, independent component analysis, and their relation to biological vision. Next, a face image representation using independent component analysis (ICA) is developed, which is an unsupervised learning technique based on optimal information transfer between neurons. The ICA representation is compared to a number of other face representations including eigenfaces and Gabor wavelets on tasks of identity recognition and expression analysis. Finally, methods for learning features that are robust to changes in viewpoint and lighting are presented. These studies provide evidence that encoding input dependencies through unsupervised learning is an effective strategy for face recognition.
Face Image Analysis by Unsupervised Learning is suitable as a secondary text for a graduate-level course, and as a reference for researchers and practitioners in industry.

Reviews

`Marian Bartlett's comparison of ICA with other algorithms on the recognition of facial expressions is perhaps the most thorough analysis we have of the strengths and limits of ICA as a preprocessing stage for pattern recognition.'
T.J. Sejnowski, Salk Institute

Table of contents (8 chapters)

  • Summary

    Bartlett, Marian Stewart

    Pages 1-4

  • Introduction

    Bartlett, Marian Stewart

    Pages 5-38

  • Independent Component Representations for Face Recognition

    Bartlett, Marian Stewart

    Pages 39-67

  • Automated Facial Expression Analysis

    Bartlett, Marian Stewart

    Pages 69-82

  • Image Representations for Facial Expression Analysis: Comparative Study I

    Bartlett, Marian Stewart

    Pages 83-99

Buy this book

eBook $119.00
price for USA in USD (gross)
  • ISBN 978-1-4615-1637-8
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $159.99
price for USA in USD
  • ISBN 978-0-7923-7348-3
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $159.99
price for USA in USD
  • ISBN 978-1-4613-5653-0
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Face Image Analysis by Unsupervised Learning
Authors
Series Title
The Springer International Series in Engineering and Computer Science
Series Volume
612
Copyright
2001
Publisher
Springer US
Copyright Holder
Springer Science+Business Media New York
eBook ISBN
978-1-4615-1637-8
DOI
10.1007/978-1-4615-1637-8
Hardcover ISBN
978-0-7923-7348-3
Softcover ISBN
978-1-4613-5653-0
Series ISSN
0893-3405
Edition Number
1
Number of Pages
XV, 173
Topics