Bartlett Face Image Analysis by Unsupervised Learning

2001
ISBN: 978-1-4615-1637-8
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 612, 173 Seiten, eBook

Reihe: The Springer International Series in Engineering and Computer Science

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, 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.
is suitable as a secondary text for a graduate-level course, and as a reference for researchers and practitioners in industry.

Zielgruppe

Research

Autoren/Hrsg.

Bartlett, Marian Stewart

Inhaltsverzeichnis

1. Summary. 2. Introduction. 3. Independent Component Representations for Face Recognition. 4. Automated Facial Expression Analysis. 5. Image Representations for Facial Expression Analysis: Comparative Study I. 6. Image Representations for Facial Expression Analysis: Comparative Study II. 7. Learning Viewpoint Invariant Representations of Faces. 8. Conclusions and Future Directions. References. Index.