Human Face Recognition Using Third-Order Synthetic Neural Networks
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 t...
| Main Authors: | , |
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| Format: | eBook |
| Language: | English |
| Published: |
New York, NY
Springer US
1997, 1997
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| Edition: | 1st ed. 1997 |
| Series: | The Springer International Series in Engineering and Computer Science
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| Subjects: | |
| Online Access: | |
| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
Table of Contents:
- 1. Introduction
- 1.1 Objective
- 1.2 Background to Neural Networks
- 1.3 Organization of book
- 2. Face Recognition
- 2.1 Background
- 2.2 Various methods
- 2.3 Neural Net Approach
- 3. Implementation of Invariances
- 3.1 Matching of similar triplets
- 3.2 Software implementation
- 4. Simple Pattern Recognition
- 4.1 Procedure
- 4.2 Results
- 5. Facial Pattern Recognition
- 5.1 Two-dimensional moment invariants
- 5.2 Face Segmentation
- 5.3 Isodensity regions
- 5.4 Reducing sensitivity to lighting conditions
- 5.5 Image encoding algorithm
- 5.6 The use of gradient images
- 6. Network Training
- 6.1 Training algorithms
- 6.2 Modifications to training algorithms
- 6.3 Training image data
- 6.4 Results
- 7. Conclusions amp; Contributions 111
- 8. Future Work
- 8.1 Simultaneous Training on all four Isodensity Images
- 8.2 Higher-resolution coarse image size
- 8.3 Automatic face recognition
- 8.4 MIMO third-order networks
- 8.5 Zernike and Complex moments
- 8.6 Recognition of facial expressions (moods)
- Index 119