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human face recognition problem: A solution based on third-order synthetic neural networks and isodensity analysis
- Date Issued:
- 1996
- Summary:
- Third-order synthetic neural networks are applied to the recognition of isodensity facial images extracted from digitized grayscale facial images. A key property of neural networks is their ability to recognize invariances and extract essential parameters from complex high-dimensional data. In pattern recognition an input image must be recognized regardless of its position, size, and angular orientation. In order to achieve this, the neural network needs to learn the relationships between the input pixels. Pattern recognition requires the nonlinear subdivision of the pattern space into subsets representing the objects to be identified. Single-layer neural networks can only perform linear discrimination. However, multilayer first-order networks and high-order neural networks can both achieve this. The most significant advantage of a higher-order net over a traditional multilayer perceptron is that invariances to 2-dimensional geometric transformations can be incorporated into the network and need not be learned through prolonged training with an extensive family of exemplars. It is shown that a third-order network can be used to achieve translation-, scale-, and rotation-invariant recognition with a significant reduction in training time over other neural net paradigms such as the multilayer perceptron. A model based on an enhanced version of the Widrow-Hoff training algorithm and a new momentum paradigm are introduced and applied to the complex problem of human face recognition under varying facial expressions. Arguments for the use of isodensity information in the recognition algorithm are put forth and it is shown how the technique of coarse-coding is applied to reduce the memory required for computer simulations. The combination of isodensity information and neural networks for image recognition is described and its merits over other image recognition methods are explained. It is shown that isodensity information coupled with the use of an "adaptive threshold strategy" (ATS) yields a system that is relatively impervious to image contrast noise. The new momentum paradigm produces much faster convergence rates than ordinary momentum and renders the network behaviour independent of its training parameters over a broad range of parameter values.
Title: | The human face recognition problem: A solution based on third-order synthetic neural networks and isodensity analysis. |
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Name(s): |
Uwechue, Okechukwu A. Florida Atlantic University, Degree grantor Pandya, Abhijit S., Thesis advisor College of Engineering and Computer Science Department of Computer and Electrical Engineering and Computer Science |
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Type of Resource: | text | |
Genre: | Electronic Thesis Or Dissertation | |
Date Issued: | 1996 | |
Publisher: | Florida Atlantic University | |
Place of Publication: | Boca Raton, Fla. | |
Physical Form: | application/pdf | |
Extent: | 170 p. | |
Language(s): | English | |
Summary: | Third-order synthetic neural networks are applied to the recognition of isodensity facial images extracted from digitized grayscale facial images. A key property of neural networks is their ability to recognize invariances and extract essential parameters from complex high-dimensional data. In pattern recognition an input image must be recognized regardless of its position, size, and angular orientation. In order to achieve this, the neural network needs to learn the relationships between the input pixels. Pattern recognition requires the nonlinear subdivision of the pattern space into subsets representing the objects to be identified. Single-layer neural networks can only perform linear discrimination. However, multilayer first-order networks and high-order neural networks can both achieve this. The most significant advantage of a higher-order net over a traditional multilayer perceptron is that invariances to 2-dimensional geometric transformations can be incorporated into the network and need not be learned through prolonged training with an extensive family of exemplars. It is shown that a third-order network can be used to achieve translation-, scale-, and rotation-invariant recognition with a significant reduction in training time over other neural net paradigms such as the multilayer perceptron. A model based on an enhanced version of the Widrow-Hoff training algorithm and a new momentum paradigm are introduced and applied to the complex problem of human face recognition under varying facial expressions. Arguments for the use of isodensity information in the recognition algorithm are put forth and it is shown how the technique of coarse-coding is applied to reduce the memory required for computer simulations. The combination of isodensity information and neural networks for image recognition is described and its merits over other image recognition methods are explained. It is shown that isodensity information coupled with the use of an "adaptive threshold strategy" (ATS) yields a system that is relatively impervious to image contrast noise. The new momentum paradigm produces much faster convergence rates than ordinary momentum and renders the network behaviour independent of its training parameters over a broad range of parameter values. | |
Identifier: | 9780591029291 (isbn), AAI9637207 (UnM), 12464 (digitool), FADT12464 (IID), fau:9358 (fedora) | |
Collection: | FAU Electronic Theses and Dissertations Collection | |
Note(s): |
College of Engineering and Computer Science Thesis (Ph.D.)--Florida Atlantic University, 1996. |
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Subject(s): |
Image processing Face perception Neural networks (Computer science) |
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Held by: | Florida Atlantic University Libraries | |
Persistent Link to This Record: | http://purl.flvc.org/fcla/dt/12464 | |
Sublocation: | Digital Library | |
Use and Reproduction: | Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder. | |
Use and Reproduction: | http://rightsstatements.org/vocab/InC/1.0/ | |
Host Institution: | FAU | |
Is Part of Series: | Florida Atlantic University Digital Library Collections. |