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Face Image Analysis by Unsupervised Learning (The Kluwer International Series in Engineering and Computer Science, Volume 612) (The Springer International Series in Engineering and Computer Science) | 
 enlarge | Manufacturer: Springer
Category: EBooks
List Price: $119.00
Buy New: $95.20
You Save: $23.80 (20%)
Avg. Customer Rating:  1 reviews
Sales Rank: 97000
Format: Kindle Book
Media: Kindle Edition
Edition: 1
Number Of Items: 1
Pages: 192
Shipping Weight (lbs): 1
Dimensions (in): 9.2 x 6.1 x 0.5
Dewey Decimal Number: 006.42
ASIN: B000PY3TTA
Publication Date: May 1, 2001
Availability: Usually ships in 24 hours
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| Editorial Reviews:
Product Description
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.
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| Customer Reviews:
Important new work in image processing of faces June 26, 2001
0 out of 1 found this review helpful
Marian Stewart Bartlett is a talented young scientist who has done some breakthrough work in the image processing of faces, particularly for the analysis of facial expressions. This book describes the details of that work, and places it in the broader context of other work in unsupervised learning and image processing. A must read for those keeping up with the latest and greatest in face image analysis!
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