Face Image Analysis by Unsupervised Learning (The Springer International Series in Engineering and Computer Science, 612)

Bartlett, Marian Stewart

ISBN 10: 0792373480 / ISBN 13: 9780792373483

Published by Springer (edition 2001), 2001

About this Item

It's a preowned item in good condition and includes all the pages. It may have some general signs of wear and tear, such as markings, highlighting, slight damage to the cover, minimal wear to the binding, etc., but they will not affect the overall reading experience.

Seller Inventory # 0792373480-11-1

Bibliographic Details

• Title: Face Image Analysis by Unsupervised Learning (The Springer International Series in Engineering and Computer Science, 612)
• Publisher: Springer (edition 2001)
• Publication Date: 2001
• Language: English
• ISBN 10: 0792373480
• ISBN 13: 9780792373483
• Binding: Hardcover
• Edition: 2001.
• Condition: Good

About this title

Synopsis

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.

Review

`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