Face Image Analysis

by Unsupervised Learning

Marian Stewart Bartlett

Kluwer International Series on Engineering and Computer Science, V. 612. Boston: Kluwer
Academic Publishers, 2001. Ordering Information (888)-640-7378. Also available on Amazon.com.

Foreword by Terrence J. Sejnowski

Table of Contents

Book Jacket

Face Image Analysis by Unsupervised Learning explores adaptive approaches to face 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, [this book] 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 practioners in industry.

"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, The Salk Institute

May 7, 2001: