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Human Face Recognition Using Third-Order Synthetic Neural Networks: The Springer International Series in Engineering and Computer Science, cartea 410

Autor Okechukwu A. Uwechue, Abhijit S. Pandya
en Limba Engleză Paperback – 12 oct 2012
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.
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Specificații

ISBN-13: 9781461368328
ISBN-10: 1461368324
Pagini: 144
Ilustrații: XV, 123 p.
Dimensiuni: 155 x 235 x 8 mm
Greutate: 0.21 kg
Ediția:Softcover reprint of the original 1st ed. 1997
Editura: Springer Us
Colecția Springer
Seria The Springer International Series in Engineering and Computer Science

Locul publicării:New York, NY, United States

Public țintă

Research

Descriere

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.

Cuprins

Preface. List of Illustrations. List of Tables. 1. Introduction. 2. Face Recognition. 3. Implementation of Invariances. 4. Simple Pattern Recognition. 5. Facial Pattern Recognition. 6. Network Training. 7. Conclusions and Contributions. 8. Future Work. Index.