Face Image Analysis
by Unsupervised Learning
Marian Stewart Bartlett
Kluwer International Series on Engineering and
Computer Science, V. 612. Boston: Kluwer
Academic Publishers, 2001.
(888)-640-7378. Also available on Amazon.com.
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
- T.J. Sejnowski, The
May 7, 2001: