Human Face Recognition Using Third-Order Synthetic Neural Networks
General Material Designation
[Book]
First Statement of Responsibility
by Okechukwu A. Uwechue, Abhijit S. Pandya.
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
Boston, MA :
Name of Publisher, Distributor, etc.
Imprint: Springer,
Date of Publication, Distribution, etc.
1997.
SERIES
Series Title
Springer International Series in Engineering and Computer Science, Multimedia Systems and Applications,
Volume Designation
410
ISSN of Series
0893-3405 ;
SUMMARY OR ABSTRACT
Text of Note
Human Face Recognition Using Third-Order Synthetic Neural Networks explores the viability of the application of High-order synthetic neural network technology to transformation-invariant recognition of complex visual patterns. High-order networks require little training data (hence, short training times) and have been used to perform transformation-invariant recognition of relatively simple visual patterns, achieving very high recognition rates. The successful results of these methods provided inspiration to address more practical problems which have grayscale as opposed to binary patterns (e.g., alphanumeric characters, aircraft silhouettes) and are also more complex in nature as opposed to purely edge-extracted images - human face recognition is such a problem. Human Face Recognition Using Third-Order Synthetic Neural Networks serves as an excellent reference for researchers and professionals working on applying neural network technology to the recognition of complex visual patterns.