A prototype for palm print identity verification using hierarchical neural network architecture

Date of Publication

1999

Document Type

Master's Thesis

Degree Name

Master of Science in Information Technology

Subject Categories

OS and Networks | Systems Architecture

College

College of Computer Studies

Department/Unit

Information Technology

Thesis Adviser

Maria P. Alvarez

Defense Panel Chair

Philip Chan

Defense Panel Member

Dr. Florante Salvador

Abstract/Summary

A system for identifying a person using biometrics technology and artificial neural systems implemented using software simulation is developed in this study. The system extracts the features of the major creases in a person's palm from its digitized image and verifies the identity of the person against a list of enrolled identities. The software simulation has been implemented by the hierarchical neural network architecture consists of a self-organizing map (SOM) to select and extract features of the palm creases from the digitized image and an association layer as feature map classifier. The SOM makes use of competitive learning algorithm while the association layer uses backpropagation neural network (BPNN) architecture through supervised learning. A unique set of codes has been gathered out of the features of the palm creases of each individual. With the neural network's inherent capability for pattern recognition, the system was able to generate similar codes, if not exactly the same codes, for palm images belonging to the same individual. This became the basis for identity verification of the system.

Abstract Format

html

Language

English

Format

Print

Accession Number

TG02869

Shelf Location

Archives, The Learning Commons, 12F Henry Sy Sr. Hall

Physical Description

181 leaves

Keywords

Identification; Palm prints; Anthropometry; Computer network architecture; Neural network (Computer science)

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