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Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 612)
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About this book
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.
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Table of contents (8 chapters)
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T.J. Sejnowski, Salk Institute
Authors and Affiliations
Bibliographic Information
Book Title: Face Image Analysis by Unsupervised Learning
Authors: Marian Stewart Bartlett
Series Title: The Springer International Series in Engineering and Computer Science
DOI: https://doi.org/10.1007/978-1-4615-1637-8
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Springer Science+Business Media New York 2001
Hardcover ISBN: 978-0-7923-7348-3Published: 30 June 2001
Softcover ISBN: 978-1-4613-5653-0Published: 26 October 2012
eBook ISBN: 978-1-4615-1637-8Published: 06 December 2012
Series ISSN: 0893-3405
Edition Number: 1
Number of Pages: XV, 173
Topics: User Interfaces and Human Computer Interaction, Computer Imaging, Vision, Pattern Recognition and Graphics, Artificial Intelligence, Statistics for Life Sciences, Medicine, Health Sciences, Control, Robotics, Mechatronics, Theory of Computation