Fingerprint authentication using a hybrid intelegent swarm

Dissertant

Tahir, Khawlah Jalil

Thesis advisor

Isa, Abbas Husayn

University

University of Technology

Faculty

-

Department

Department of Electrical Engineering

University Country

Iraq

Degree

Master

Degree Date

2013

English Abstract

Fingerprint authentication and recognition is an important subject that has been widely used in various applications because of its reliability and accuracy in the process of authenticating and recognizing the person's identity.

In this work, an Intelligent Fingerprint Authentication Model (IFAM) based upon the Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) has been proposed.

The proposed work consists of two main phases which are the features extraction and the authentication.

The features extraction phase has been regarded via proposing a statistical and geometrical approach for determining and isolating the features of the fingerprint images.

The proposed approach is called the Features Ring Approach which is abbreviated by FRA.

The approach creates a circular ring centered at the core point of the fingerprint to bind the valuable features that are invariant under rotation and translation.

The radius of the outer circle of the ring is suggested to be variable to give a variable area for the established circular ring.

The authentication phase of IFAM suggests the neural network to hold the job of verification of the extracted feature patterns resulted by FRA for a fingerprint image of certain person.

This is done using a neural network trained with a collection of features patterns extracted from fingerprint images.

Particle Swarm Optimization (PSO) is suggested as a hybrid training algorithm for the structured neural network, while the backpropagation (BP) algorithm is used for the comparison and evaluation purposes.

The proposed IFAM is investigated in forms of convergence time and testing accuracy.

The results show that PSO-NN significantly needs less time for the convergence compared with BPNN.

For the testing accuracy percentage; they showed that PSO-NN result is better than BP-NN with (93.33%) compared with (86.66%).

The software package MATLAB (2012a) is used in writing all the programs.

Main Subjects

Electronic engineering

Topics

• Data processing
• Neural networks(Computer science)

• APA
• MLA
• AMA

Language

English

Data Type

Arab Theses

Record ID

BIM-418066