Overview

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. `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

Full Product Details

Author:   Marian Stewart Bartlett
Publisher:   Springer
Imprint:   Springer
Edition:   2001 ed.
Volume:   612
Dimensions:   Width: 15.50cm , Height: 1.20cm , Length: 23.50cm
Weight:   1.000kg
ISBN:  

9780792373483

ISBN 10:   0792373480
Pages:   173
Publication Date:   30 June 2001
Audience:   College/higher education ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

1 An Introduction to Acoustic Echo and Noise Control.- 1. Human Perception of Echoes.- 2. The Network Echo Problem.- 3. The Acoustic Echo Problem.- 4. Adaptive Filters for Echo Cancellation.- 5. Noise Reduction.- 6. Conclusions.- I Mono-Channel Acoustic Echo Cancellation.- 2 The Fast Affine Projection Algorithm.- 3 Subband Acoustic Echo Cancellation Using the FAP-RLS Algorithm: Fixed-Point Implementation Issues.- 4 Real-Time Implementation of the Exact Block NLMS Algorithm for Acoustic Echo Control in Hands-Free Telephone Systems.- 5 Double-Talk Detection Schemes for Acoustic Echo Cancellation.- II Multi-Channel Acoustic Echo Cancellation.- 6 Multi-Channel Sound, Acoustic Echo Cancellation, and Multi-Channel Time-Domain Adaptive Filtering.- 7 Multi-Channel Frequency-Domain Adaptive Filtering.- 8 A Real-time Stereophonic Acoustic Subband Echo Canceler.- III Noise Reduction Techniques with a Single Microphone.- 9 Subband Noise Reduction Methods for Speech Enhancement.- IV Microphone Arrays.- 10 Superdirectional Microphone Arrays.- 11 Microphone Arrays for Video Camera Steering.- 12 Nonlinear, Model-Based Microphone Array Speech Enhancement.- V Virtual Sound.- 13 3D Audio and Virtual Acoustical Environment Synthesis.- 14 Virtual Sound Using Loudspeakers: Robust Acoustic Crosstalk Cancellation.- VI Blind Source Separation.- 15 An Introduction to Blind Source Separation of Speech Signals.

Reviews

`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

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

'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

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