Date of Award

Fall 2002

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Electrical & Computer Engineering

Program/Concentration

Computer Engineering

Committee Director

Vijayan K. Asari

Committee Member

Linda Vahala

Committee Member

Min Song

Call Number for Print

Special Collections LD4331.E55 R35 2002

Abstract

The design and development of the digital implementation of a multilevel feed forward neural network architecture for face recognition based on statistical features representing Eigenfaces is presented in this thesis. The architecture is divided into three parts: feature extractor, classifier and identifier, The Eigenface extractor architecture is developed based on an efficient design strategy in which all the M weight values corresponding to the Eigenfaces are generated simultaneously from M images representing the Eigen vectors and the test input image. The multilayer neural network classifier is trained using error backpropagation algorithm. A novel multilevel digital architecture is developed for the implementation of the multilayer feed forward neural network for categorization of the input vectors into specific output classes. At the output of the backpropagation network, a maximization network is used for the final classification of the multilevel outputs from the neural network. The architecture is simulated by Alters Quattus II and implemented in APEX 20K series FPGA. The data interpretation concepts adopted in the system design led to an efficient design methodology, which eliminated the necessity of complex computations needed for the implementation of multilayer perceptron using sigmoid function.

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DOI

10.25777/pnn7-tq03

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