Fingerprint Recognition System.
FINGERPRINT RECOGNITION SYSTEM
NUR AINA BINTI ZAINAL
This draft is submitted in partial fulfilment of the requirements for the award of Bachelor of Electronic Engineering (Industrial Electronics) With Honour
Faculty of Electronic and Computer Engineering Universiti Teknikal Malaysia Melaka
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30 April 2009 30 April 2009
24 Taman Sri Putih, Jalan Tok Ungku, 70300 Seremban, N. Sembilan
NUR AINA BINTI ZAINAL 2005 - 2009
FINGERPRINT RECOGNITION SYSTEM
UNIVERSTI TEKNIKAL MALAYSIA MELAKA
FAKULTI KEJURUTERAAN ELEKTRONIK DAN KEJURUTERAAN KOMPUTER
BORANG PENGESAHAN STATUS LAPORAN PROJEK SARJANA MUDA II
Tajuk Projek : ………
Sesi Pengajian : ………
Saya ……….. (HURUF BESAR)
mengaku membenarkan Laporan Projek Sarjana Muda ini disimpan di Perpustakaan dengan syarat-syarat kegunaan seperti berikut:
1. Laporan adalah hakmilik Universiti Teknikal Malaysia Melaka.
2. Perpustakaan dibenarkan membuat salinan untuk tujuan pengajian sahaja.
3. Perpustakaan dibenarkan membuat salinan laporan ini sebagai bahan pertukaran antara institusi pengajian tinggi.
4. Sila tandakan ( √ ) :
SULIT*
(Mengandungi maklumat yang berdarjah keselamatan atau kepentingan Malaysia seperti yang termaktub di dalam AKTA RAHSIA RASMI 1972)
TERHAD* (Mengandungi maklumat terhad yang telah ditentukan oleh
organisasi/badan di mana penyelidikan dijalankan)
TIDAK TERHAD
Disahkan oleh:
__________________________ ___________________________________ (TANDATANGAN PENULIS) (COP DAN TANDATANGAN PENYELIA) Alamat Tetap: ………...
………...
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NUR AINA BINTI ZAINAL 30 APRIL 2009
“I hereby declare that this report is the result of my own work except for quotes as cited in the references.”
Signature : ... Author : ... Date : ...
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NOOR ASYIKIN BINTI SULAIMAN 30 APRIL 2009
“I hereby declare that I have read this report and in my opinion this report is sufficient in terms of the scope and quality for the award of Bachelor of Electronic
Engineering (Industrial Electronics) With Honour.”
Signature : ... Supervisor’s Name : ... Date : ...
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Dedicated especially to my beloved mom and dad Zainal Bin Hashim and Sarimah Binti Tunas
Supervisor
Noor Asyikin Binti Sulaiman Siblings
Nur Idayati, Mohd Amri, Nur Shakila, Nur Fathihah and Mohd Walin Someone special
Mohd Zaidi Bin Abdul Manap Housemate, classmate and friends
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ACKNOWLEDGEMENT
In the name of Allah, the Most Beneficent and the Most Merciful, I would like to express my grateful to Him for giving me the strength and the ability to complete this thesis.
To my supervisor Pn. Noor Asyikin Sulaiman, thanks a lot for her guidance, help and support to accomplish this thesis. I also would like to thanks my family who had been supporting me until this project is completed.
Lastly, for my friends who have directly or indirectly contributed in helping me to complete this project.
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ABSTRACT
Fingerprints are part of human identity. Fingerprints are the most mature biometric; automated method of recognition a person based on physical or behavioral characteristics technology. In this thesis, a neural network in MATLAB was used to train and simulated data of fingerprint. A multi-layer neural network based on the popular feed-forward (FF) algorithm has been used for classification. At the end, all the simulate network will interface with Graphical User Interface for user application.
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ABSTRAK
Cap jari merupakan sebahagian daripada identiti manusia. Cap jari juga adalah biometrik yang paling matang; kaedah automatic teknologi dalam mengenali individu berdasarkan pada sifat fizikal ataupun tabiat. Dalam tesis ini, “neural network toolbox” didalam Matlab digunakan untuk melatih dan menganalisa data cap jari. Rangkaian “multi-layer” berdasarkan pada algoritma popular iaitu “fd-forward” digunakan untuk mengklasifikasi cap jari. Pada akhirnya, kesemua data yang telah disimulasi akan berhubung kait dengan “Graphical User Interface (GUI)” aplikasi.
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TABLE OF CONTENT
CHAPTER DESCRIPTION PAGE
TITLE OF THE PROJECT i
DISCLAIMER iii
SUPERVISOR’S CONFIRMATION iv
DEDICATION v
ACKNOWLEDGEMENT vi
ABSTRACT vii
ABSTRAK viii
TABLE OF CONTENTS ix
LIST OF TABLES xii
LIST OF FIGURES xiii
LIST OF ABBREVIATIONS xv
1 INTRODUCTION
1.1 Background 1
1.2 Objectives of Project 1
1.3 Problem Statement 2
1.4 Scope of Work 2
1.5 Methodology 2
1.6 Outline of the project report 3
2 LITERATURE REVIEW
2.1 Biometric Features 5
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2.2.1 Fingerprint Patterns 7
2.3 Fingerprint Recognition 9
2.4 Pattern Recognition 10
2.5 Matlab Software 12
2.5.1 The Matlab System 13
2.5.1.1 Development Environment 13 2.5.1.2 The Matlab Mathematical
Function Library 13 2.5.1.3 The Matlab Language 13 2.5.1.4 Handle Graphics 14 2.5.1.5 The Matlab Application
Program Interface (API) 14
2.6 Toolboxes 14
2.7 Neural Networks Definition 15
2.7.1 Backpropagation Neural Network 14 2.8 Multi-layer feed-forward network 16 2.8.1 Neuron Model (tansig, logsig, purelin) 16 2.8.2 Feed-forward Network 18
2.9 The Feed-forward Algorithm 20
3 PROJECT METHODOLOGY
3.1 Introduction 23
3.2 Flow Chart 23
3.3 Software 26
3.3.1 Neural Network Toolbox in Matlab 26
3.4 GUI using GUIDE 30
3.4.1 Designing GUI 31
4 RESULT AND ANALYSIS
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4.2 Circuit Designing and Network Build 34
4.3 Training Result 35
4.4 Output Result of GUI 40
4.5 Final Output Result 41
5 CONCLUSION
5.1 Conclusion 48
5.2 Suggestions 49
REFERENCES 50
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LIST OF TABLES
NO TITLE PAGE
2.1 Comparison of various biometric recognitions 6
3.1 Input 1 27
3.2 Input 2 28
3.3 Target 28
3.4 GUIDE component used 32
4.1 Output training data for input 1 after 2500 epochs 36
4.2 Output training data for input 1 39
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LIST OF FIGURES
NO TITLE PAGE
1.1 Methodology of the project 3
2.1 Type of arch patterns 7
2.2 Type of loop patterns 8
2.3 Type of whorl patterns 9
2.4 An elementary neuron with R inputs 17
2.5 Expend of neuron layers 17
2.6 Log-sigmoid transfer function 17
2.7 Tan-sigmoid transfer function 18
2.8 Linear transfer function 18
2.9 Single Layer Network 19
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3.1 Major components of fingerprint recognition system 24
3.2 Flow chart of methodology 25
3.3 Network Data Manager 27
3.4 Block diagram for network 29
3.5 Training parameters setting 29
3.6 Weight settings 30
3.7 Open the GUIDE 31
3.8 GUIDE Quick Start Menus (Creating New GUI) 32
3.9 List of component in GUIDE’s layout 33
4.1 Training data 1 for 2500 epochs 35
4.2 Training data 1 for second 2500 epochs 36
4.3 Training data 1 for 5000 epochs 37
4.4 Training data 1 for 10000 epochs 37
4.5 Training data 1 for 15000 epochs 38
4.6 Complete GUI for neural network’s fingerprint recognition 40
4.7 Clicking the “NNTool” button 41
4.8 A complete network data manager to simulate 42
4.9 Click simulate button 43
4.10 Simulate window 43
4.11 Complete simulate window 44
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4.13 Export output simulation to Workspace in Matlab 46 4.14 Pop-up to export or save data form network data manager 46
4.15 FeedForward_output in GUI 47
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LIST OF ABBREVIATIONS
DNA - Deoxyribonucleic acid
MATLAB - Matrix Laboratory
AFIS - Automated Fingerprint Identification System
NNTool - Neural Network Toolbox
FF - Feed-forward
MLP - Multilayer Perception
RBF - Radial-Basis Function
ANN - Artificial Neural Network
GUI - Graphical User Interface
GUIDE - GUI in Matlab
M-file - Matlab coding file
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CHAPTER 1
INTRODUCTION
1.1 Background
For the past many years, different types of automatic personal identification method have been developed and market using human biometrics features. Human biometrics features divide into two categories; physiological features and behavioural features. Physiological features such as face, fingerprint, hand, iris and DNA where as behavioural features such keystroke, signature and voice. Fingerprints are one of the most popular and reliable biometrics features for verifying a person’s identification. It usually uses to prevent user confidential information. The fingerprint recognition is the process of verifying the user’s identity by checking the fingerprint.
1.2 Objectives of project
The major objective of this project is to develop a system that can recognize fingerprint. In this project, only a small number of fingerprint data people were selected to be trained in Neural Network Toolbox (NNTool). All simulations performed by using Matlab which specific in NNTool.
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1.3 Problem statement
Nowadays security is the most important thing especially in application of a bank-machine, a network and others. Everyone is known to have unique, immutable fingerprint. A fingerprint is made of a series of ridges and furrows on the surface of the finger. The uniqueness of a fingerprint can be determined by pattern of ridges and furrow as well as the minutiae points. So fingerprint identification is one of the cheapest and the best way for security system.
1.4 Scope of work
The scope of this project is to develop a system that can recognize fingerprint by using Matlab. Fingerprint data selected was used in this project as described in [1]. All simulations are performed using Matlab/NNTool. The neural network is trained to learn and distinguish between match and non-match features of fingerprint. All trained data were interfaced with Graphical User Interface (GUI) to recognize fingerprint based on test data.
1.5 Methodology
This project is designed to identify the type of fingerprint. There are several project planning as described by following flow chart to complete this project. This methodology was used to accomplish this project using Neural Network Tool (NNTool) in Matlab. After that, an appropriate data of fingerprint extracts will be used as data train. A complete methodology for this project is described in Figure 1.1.
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Figure 1.1 Methodology of the project
1.6 Outline of the project report
This thesis is divided into five chapters. Chapter One, is an introduction on fingerprint recognition for security implemented. This chapter also discusses about the aim of this project, problem statement and methodology for this project. Chapter Two includes some research of biometrics features, classification of fingerprint patterns and fingerprint recognition. Chapter Three explains all methodology that
Start
Research of project
Choose the right software and network to used in this project
Find the suitable fingerprint extract
Build the network
Compile and simulate
Testing the network
Checking any error
Last testing
End
Debug
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used for this project. It is including the software design, Neural Network Toolbox and Feed-Forward algorithm.
Chapter Four is followed with simulation results. And last chapter which is Chapter Five is a conclusion and summary of a project done. Further works based on the findings of the projects are also suggested in this chapter.
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CHAPTER 2
LITERATURE REVIEW
2.1 Biometrics features
Biometrics recognition is an automated recognition of individuals based on their behavioral and biological characteristics. Biometric recognition is measuring an individual's suitable behavioral and biological characteristics in a recognition inquiry and comparing these data with the biometric reference data which had been stored during a learning procedure, the identity of a specific user is determined.
Biometrics characteristic can be divided in two main classes which is physiological and behavioral biometric. Physiological is related to the shape of the body. The oldest traits, which have been used for more than 100 years, are fingerprints. Other examples are face recognition, hand geometry and iris recognition. Behavioral is related to the behavior of a person. The first characteristic to be used, still widely used today, is the signature. More modern approaches are the study of keystroke dynamics and of voice. The comparisons of various biometric recognitions are shown by table below.
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Table 2.1 Comparison of various biometric recognitions
2.2 Introduction of Fingerprint
Fingerprints are the tiny ridges, whorls and valley patterns on the tip of each finger. They form from pressure on baby’s tiny developing fingers in the womb. No two people have been found to have the same fingerprints; they are totally unique. There’s a one in 64 billion chance that your fingerprint will match up exactly with someone else [10]. Fingerprints are even more unique than Deoxyribonucleic acid (DNA), the genetic material in each of our cells. Although identical twins can share the same DNA but they cannot have the same fingerprints.
Fingerprinting is one form of biometrics, a science that uses people’s physical characteristic to identify them. Fingerprints are ideal for this purpose because they are inexpensive to collect and analyze and they are never change, even as people age. It has been proved as presented in Table 2.1. Although hands and feet have many ridged areas that could be used for identification, fingerprints became popular form of biometrics because they are easy to classify and sort. They are also accessible.
Legend L = Low M = Medium H = High
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2.2.1 Fingerprint patterns
Fingerprints are made of an arrangement of ridges, called friction ridges. All of the ridges of fingerprints form patterns called loops, whorl and arches.
Arches are found in about 5% of fingerprint patterns encountered. The ridges run from one side to the other of the pattern, making no backward turn. Ordinarily, there is no delta in an arch pattern but where there is a delta; no re-curving ridge must intervene between the core and delta points. There are four types of arch patterns: plain arches, radial arches, ulnar arches and tented arches. Plain arches have an even flow of ridges from one side to the other of the pattern; no “significant up thrusts” and the ridges enter on one side of the impression, and flow out the other with a rise or wave in the center. The ridges of radial arches slope towards the thumb, have one delta and no re-curving ridges. On ulnar arches, the ridges slope towards the little finger, have one delta and no re-curving ridges. Tented arches have an angle, an up thrust, or two of the three basic characteristics of the loop. They don’t have the same "easy" flow that plain arches do and particularly have “significant up thrusts” in the ridges near the middle that arrange themselves on both sides of a spine or axis towards which the adjoining ridges converge and appear to form tents.
Figure 2.1 Types or arch patterns
Loops occur in about 60-70 % of fingerprint patterns encountered. One or more of the ridges enters on either side of the impression, re-curves, touches or crosses the line running from the delta to the core and terminates on or in the direction of the side where the ridge or ridges entered. Each loop pattern has is one
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delta and one core and has a ridge count. Radial loops are named after the radius, a bone in the forearm that joins the hand on the same side as the thumb. The flow of the pattern in radial loops runs in the direction of the radius (toward the thumb). Radial loops are not very common and most of the time radial loops will be found on the index fingers. Ulnar loops are named after the ulna, a bone in the forearm. The ulna is on the same side as the little finger and the flow of the pattern in an ulnar loop runs in the direction of the ulnar (toward the little finger).
Figure 2.2 Types of loop patterns
Whorls are seen in about 25-35 % of fingerprint patterns encountered. In a whorl, some of the ridges make a turn through at least one circuit. Any fingerprint pattern which contains 2 or more deltas will be a whorl pattern. There are four types of whorl patterns. Plain whorls consist of one or more ridges which make or tend to make a complete circuit with two deltas, between which an imaginary line is drawn and at least one re-curving ridge within the inner pattern area is cut or touched. Central pocket loop whorls consist of at least one re-curving ridge or an obstruction at right angles to the line of flow, with two deltas, between which when an imaginary line is drawn, no re-curving ridge within the pattern area is cut or touched. Central pocket loop whorl ridges make one complete circuit which may be spiral, oval, circular or any variant of a circle. Double loop whorls consist of two separate and distinct loop formations with two separate and distinct shoulders for each core, two deltas and one or more ridges which make, a complete circuit. Between the two at least one re-curving ridge within the inner pattern area is cut or touched when an imaginary line is drawn. Accidental whorls consist of two different types of patterns with the exception of the plain arch; have two or more deltas or a pattern which
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Figure 1.1 Methodology of the project
1.6 Outline of the project report
This thesis is divided into five chapters. Chapter One, is an introduction on fingerprint recognition for security implemented. This chapter also discusses about the aim of this project, problem statement and methodology for this project. Chapter Two includes some research of biometrics features, classification of fingerprint patterns and fingerprint recognition. Chapter Three explains all methodology that
Start
Research of project
Choose the right software and network to used in this project
Find the suitable fingerprint extract
Build the network
Compile and simulate
Testing the network
Checking any error
Last testing
End
Debug
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used for this project. It is including the software design, Neural Network Toolbox and Feed-Forward algorithm.
Chapter Four is followed with simulation results. And last chapter which is Chapter Five is a conclusion and summary of a project done. Further works based on the findings of the projects are also suggested in this chapter.
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CHAPTER 2
LITERATURE REVIEW
2.1 Biometrics features
Biometrics recognition is an automated recognition of individuals based on their behavioral and biological characteristics. Biometric recognition is measuring an individual's suitable behavioral and biological characteristics in a recognition inquiry and comparing these data with the biometric reference data which had been stored during a learning procedure, the identity of a specific user is determined.
Biometrics characteristic can be divided in two main classes which is physiological and behavioral biometric. Physiological is related to the shape of the body. The oldest traits, which have been used for more than 100 years, are fingerprints. Other examples are face recognition, hand geometry and iris recognition. Behavioral is related to the behavior of a person. The first characteristic to be used, still widely used today, is the signature. More modern approaches are the study of keystroke dynamics and of voice. The comparisons of various biometric recognitions are shown by table below.
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Table 2.1 Comparison of various biometric recognitions
2.2 Introduction of Fingerprint
Fingerprints are the tiny ridges, whorls and valley patterns on the tip of each finger. They form from pressure on baby’s tiny developing fingers in the womb. No two people have been found to have the same fingerprints; they are totally unique. There’s a one in 64 billion chance that your fingerprint will match up exactly with someone else [10]. Fingerprints are even more unique than Deoxyribonucleic acid (DNA), the genetic material in each of our cells. Although identical twins can share the same DNA but they cannot have the same fingerprints.
Fingerprinting is one form of biometrics, a science that uses people’s physical characteristic to identify them. Fingerprints are ideal for this purpose because they are inexpensive to collect and analyze and they are never change, even as people age. It has been proved as presented in Table 2.1. Although hands and feet have many ridged areas that could be used for identification, fingerprints became popular form of biometrics because they are easy to classify and sort. They are also accessible.
Legend L = Low M = Medium H = High
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2.2.1 Fingerprint patterns
Fingerprints are made of an arrangement of ridges, called friction ridges. All of the ridges of fingerprints form patterns called loops, whorl and arches.
Arches are found in about 5% of fingerprint patterns encountered. The ridges run from one side to the other of the pattern, making no backward turn. Ordinarily, there is no delta in an arch pattern but where there is a delta; no re-curving ridge must intervene between the core and delta points. There are four types of arch patterns: plain arches, radial arches, ulnar arches and tented arches. Plain arches have an even flow of ridges from one side to the other of the pattern; no “significant up thrusts” and the ridges enter on one side of the impression, and flow out the other with a rise or wave in the center. The ridges of radial arches slope towards the thumb, have one delta and no re-curving ridges. On ulnar arches, the ridges slope towards the little finger, have one delta and no re-curving ridges. Tented arches have an angle, an up thrust, or two of the three basic characteristics of the loop. They don’t have the same "easy" flow that plain arches do and particularly have “significant up thrusts” in the ridges near the middle that arrange themselves on both sides of a spine or axis towards which the adjoining ridges converge and appear to form tents.
Figure 2.1 Types or arch patterns
Loops occur in about 60-70 % of fingerprint patterns encountered. One or more of the ridges enters on either side of the impression, re-curves, touches or crosses the line running from the delta to the core and terminates on or in the direction of the side where the ridge or ridges entered. Each loop pattern has is one
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delta and one core and has a ridge count. Radial loops are named after the radius, a bone in the forearm that joins the hand on the same side as the thumb. The flow of the pattern in radial loops runs in the direction of the radius (toward the thumb). Radial loops are not very common and most of the time radial loops will be found on the index fingers. Ulnar loops are named after the ulna, a bone in the forearm. The ulna is on the same side as the little finger and the flow of the pattern in an ulnar loop runs in the direction of the ulnar (toward the little finger).
Figure 2.2 Types of loop patterns
Whorls are seen in about 25-35 % of fingerprint patterns encountered. In a whorl, some of the ridges make a turn through at least one circuit. Any fingerprint pattern which contains 2 or more deltas will be a whorl pattern. There are four types of whorl patterns. Plain whorls consist of one or more ridges which make or tend to make a complete circuit with two deltas, between which an imaginary line is drawn and at least one re-curving ridge within the inner pattern area is cut or touched. Central pocket loop whorls consist of at least one re-curving ridge or an obstruction at right angles to the line of flow, with two deltas, between which when an imaginary line is drawn, no re-curving ridge within the pattern area is cut or touched. Central pocket loop whorl ridges make one complete circuit which may be spiral, oval, circular or any variant of a circle. Double loop whorls consist of two separate and distinct loop formations with two separate and distinct shoulders for each core, two deltas and one or more ridges which make, a complete circuit. Between the two at least one re-curving ridge within the inner pattern area is cut or touched when an imaginary line is drawn. Accidental whorls consist of two different types of patterns with the exception of the plain arch; have two or more deltas or a pattern which