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Human Face Recognition Using Third-Order Synthetic Neural Networks

  • Book
  • © 1997

Overview

Authors:
  1. Okechukwu A. Uwechue

    1. AT&T Laboratories, USA

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    You can also search for this author in PubMed   Google Scholar

  2. Abhijit S. Pandya

    1. Florida Atlantic University, USA

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    You can also search for this author in PubMed   Google Scholar

Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 410)

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Table of contents (8 chapters)

  1. Front Matter

    Pages i-xv
    Download chapter PDF

  2. Introduction

    • Okechukwu A. Uwechue, Abhijit S. Pandya
    Pages 1-20

  3. Face Recognition

    • Okechukwu A. Uwechue, Abhijit S. Pandya
    Pages 21-35

  4. Implementation of Invariances

    • Okechukwu A. Uwechue, Abhijit S. Pandya
    Pages 37-45

  5. Simple Pattern Recognition

    • Okechukwu A. Uwechue, Abhijit S. Pandya
    Pages 47-55

  6. Facial Pattern Recognition

    • Okechukwu A. Uwechue, Abhijit S. Pandya
    Pages 57-90

  7. Network Training

    • Okechukwu A. Uwechue, Abhijit S. Pandya
    Pages 91-109

  8. Conclusions & Contributions

    • Okechukwu A. Uwechue, Abhijit S. Pandya
    Pages 111-114

  9. Future Work

    • Okechukwu A. Uwechue, Abhijit S. Pandya
    Pages 115-117

  10. Back Matter

    Pages 119-123
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Keywords

About this book

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.

Authors and Affiliations

  • AT&T Laboratories, USA

    Okechukwu A. Uwechue

  • Florida Atlantic University, USA

    Abhijit S. Pandya

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