Face Image Analysis by Unsupervised Learning

  • Marian Stewart Bartlett

Table of contents

  1. Front Matter
    Pages i-xv
  2. Marian Stewart Bartlett
    Pages 1-4
  3. Marian Stewart Bartlett
    Pages 5-38
  4. Marian Stewart Bartlett
    Pages 69-82
  5. Marian Stewart Bartlett
    Pages 151-156
  6. Back Matter
    Pages 157-173

About this book

Introduction

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.

Keywords

coding cognition image analysis information information theory learning presentation supervised learning wavelet wavelets

Authors and affiliations

  • Marian Stewart Bartlett
    • 1
  1. 1.Institute for Neural ComputationUniversity of CaliforniaSan DiegoUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4615-1637-8
  • Copyright Information Kluwer Academic Publishers 2001
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-5653-0
  • Online ISBN 978-1-4615-1637-8
  • Series Print ISSN 0893-3405
  • About this book