1 UNIVERSITY OF ZIMBABWE DEPARTMENT OF COMPUTER SCIENCE Integrated Improved Security System Techniques in Combating Crime Using the Biometrics at the National Airport of Zimbabwe GODFREY SITHOLE R9917454 Supervisors : Dr G. T. Hapanyengwi Mr O. Kufandirimbwa This dissertation is submitted in partial fulfillment of the requirements for the degree of Masters of Computer Science in Computer Science 17 AUGUST 2010
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UNIVERSITY OF ZIMBABWE
DEPARTMENT OF COMPUTER SCIENCE
Integrated Improved Security System Techniques in Combating Crime
Using the Biometrics at the National Airport of Zimbabwe
GODFREY SITHOLE R9917454
Supervisors : Dr G. T. Hapanyengwi
Mr O. Kufandirimbwa
This dissertation is submitted in partial fulfillment of the requirements for
the degree of Masters of Computer Science in Computer Science
17 AUGUST 2010
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ACKNOWLEDGEMENTS
Firstly and foremost, I would like to thank my sweetheart Chiedza Sithole for her
patience and understanding during the many hours I disappear and become
antisocial. My heartfelt thanks also go to Dr G. T. Hapanyengwi and Mr O.
Kufandirimbwa my project supervisors, for their patience and guidance over the
course of the project.
I would also like to express my gratitude to Professor Mafongoya, Dr Mahamadi,
Dr Nyaruwata and Mr Rupere for their support to the completion of this project.
My gratitude also go to Mr B Nyambo the Chairperson of the University of
Zimbabwe Department of Computing Science for arranging project seminars for
us. I would like to thank Mr R. Sidimeli, Mr C. Mukwandara , Mr E. Sambana ,
Mrs G. Chirwa and Mrs P. Mushangwe of the same department for their
assistance with access to the department resources.
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DEDICATION
TO MY SON GODFREY FIDEL
WITH LOVE
TO MY MOTHER, MRS E. SITHOLE AND MY BELOVED FATHER, THE LATE
MR A. M. H. M. SITHOLE, I MISS YOU.
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TABLE OF CONTENTS
CHAPTER 1: INTRODUCTION
1.1 Background Information and Problem definition 11.2 Aim and Objectives 71.3 Scope of the study 71.4 Research Problem 81.5 Justification of the study 81.6 Overview of the thesis 8
CHAPTER 2: LITERATURE REVIEW
2.1 Introduction to review on Biometrics 102.2 Advantages and Disadvantages of Biometrics 182.3 The Properties of Biometrics 202.4 Fingerprint Matching 212.5 Fingerprint Limitations 222.6 Fingerprint Matching techniques 24
CHAPTER 3: REVIEW AND ANALYSIS OF EXISTING ALGORITHMS
3.1 Minutia Matching 273.2 Existing Matching Algorithms 283.3 Pattern based (or image-based) Algorithms 293.4 Optical 293.5 Disadvantages 303.6 Ultrasonic 303.7 Capacitance 313.8 Passive Capacitance 323.9 Active Capacitance 323.10 Minutia Matching Algorithm 333.11 Process in existing matching algorithm 333.12 Disadvantages of the existing system 343.13 Architecture of the proposed algorithms 343.14 Operations of the proposed matching algorithms 353.15 Architecture of the proposed system 363.16 Requirements of the system 373.17 Outputs 373.18 Inputs 383.19 Processes 383.20 Performance /Complexity 393.21 Non- Functional Requirements of the system 393.22 Scalable 403.23 Conclusion 40
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CHAPTER 4 CONCEPTUAL DESIGN
4.1 Introduction 424.2 Current Operations 424.3 Architecture of Existing system 434.4 Proposed Integration improved minutia algorithm 44 4.5 Proposed Solution Architecture 454.6 Components of the architecture 454.7 The Algorithm 464.8 Alignment of point patterns 484.9 Align Point Matching 484.10 The Experimental results 484.11 The user Interface 50
Chapter 5 System Implementation
5.1 Introduction 525.2 Implementation and Programming Language used 525.3 Implementing Security 535.4 Maintaining Data Integrity 535.5 Referential Integrity 5.3
Chapter 6: Testing and evaluation of the Integrated Improved security system techniques in combating crime using the Biometrics.
6.1 Material View and Simulation 546.2 Black Box Testing 556.3 Integration testing 556.4 User Interface testing 556.5 Output testing 556.6 Walkthrough testing 56 6.7 Evaluation of the Integrated Improved security techniques 57
Chapter 7: Conclusion and Suggestions for future work
7.1 Introduction 587.2 Evaluation 587.3 Limitations 587.4 Suggestions for future work 597.5 Recommendations 60
Chapter 8: System Output(Simulation , Interface, Components)
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Figures:
Figure 1: Fingerprint patterns.Figure2: Architecture of the proposed system.Figure3: Architecture of the existing system.Figure4: Minutia Extraction.Figure5: User Interface.
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Tables.
Table 1: Advantages and Disadvantages of Biometrics.Table 2: Technology ,Advantages and Disadvantages.Table 3: Matching Results.
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Diagrams61-111
REFERENCE 112 -115
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ABSTRACT
Biometrics have become very popular as a tool in combating crime in the world.
This has also been necessitated by the September 11 2001 event. Despite all
the research that has been carried out the improvement of the algorithms still has
not been exhausted.
In the past research community has focused primarily on the password, and
national identity cards and biometrics with less effective algorithms. Certainly
there are scenarios that clearly favour one approach over the other. However it
is my belief that in any of the current and future complex crimes will require better
algorithms with improved time and space complexities.
Firstly the algorithm is subjected to the fingerprint authentication. The results are
recorded. Then the algorithm is improved and better results were achieved.
Surprisingly, extremely little research to date has considered continuous
systematic and logical improving the algorithms to achieve authenticated results.
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CHAPTER 1
INTRODUCTION
1.1 Background information and Problem definition
Providing security to any place is a major concern for an organization or
country. People had resorted to different methods of securing their property
by building durawalls, electric fences, burglar bars to mention a few. The
main idea behind is for the property not to be used without the consent of the
owner. Some scholars refer using somebody’s knowledge without his /her
consent as stealing. However security refers to different forms of safe
environment. According to Maslow Highrachy of needs also mentioned
security as a major concern.
There has been a surge of work in the area recently due to increased
demand from customers. There have been many world events that have
directed our attention towards safety and security. In particular the tragic
event of September 11 2001. This event had increased the attention to
security in airports as well the general environment. Most of the attention to
security has been obvious such as improved screening of peoples access to
particular places. Does visible security actually aid computer attackers or
terrorists who play close attention to the development of such security
techniques? Would we feel safer if security were transparent to us or would it
be an invasion of privacy?. Then what about combating crime using the non-
biometrics and biometric techniques.
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This study will look at integrated improved techniques to combat crime using
the biometrics. Now it is important to provide a few details about some
aspects of international crime in the world. The organized crime is together
with terrorism, a major threat to our society. In contrary to terrorism, criminal’s
have no interest in acting publicly. That makes it difficult to fight and prevent
crime.
There are a series of factors such as
The international dimension
The sophisticated and flexible group structures.
The use of legitimate business
The degree of specialization
The attempt to influence decision
Non- biometrics refers to the identification of as person using the passwords and
identity cards amongst others. Biometrics refers to the identification of a person
based on his or her physiological on behavioral characteristics. Today they are
many biometrics devices based on characteristics that are unique for everyone.
Some of these characteristics include, but not limited to fingerprints, hand
geometry and voice . These characteristics can be used to positively identify
someone. Many biometrics are based on the capture and matching of biometrics
characteristics in order to produce a positive identification. A good example is
that if that if we place a biometric device or systems of devices at the Harare
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International Airport will be able to detect who comes in or who goes out with
criminal offences.
With every system, there are vulnerabilities that someone, if given a chance will
take advantage of . Currently, Zimbabwe is using passwords and identity cards
as a form of identification. Now it is easy to talk about vulnerabilities and to take
advantage of those vulnerabilities, but if we are aware of them, we may be able
to improve the security system that mitigates such weaknesses.
The disadvantages of passwords and identity cards is that they can be used by
somebody if stolen without the owner’s consent. In general every biometric
device or system of devices includes the following three processes enrollment,
live presentation and matching. The time of enrollment is when the user
introduces his or her biometric information to the biometric device for the first
time. The enrollment data is processed to form the stored biometric template.
Later during the live presentation user’s biometric information is extracted by the
biometric device and processed to form the live biometric template. Lastly the
stored biometric template and the life biometric template are compared to each
other at the same time of matching to provide the biometrics score in results.
Each of these processes is discussed in detail including possible faults. In this
study we will concentrate on the matching process and to improve the algorithm.
This also applies that the matching can be done by a human being as in the
issue of identity cards.
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The use of violence and the counter measures applied by organized crime
groups. The above all indicate the strength of crime activities of well organized
criminal groups.
The crime areas that seem to have appealing are organized crime groups
across most African states with Zimbabwe included are
During trafficking especially, the production and trafficking of synthetic
drugs
The exploitation of human beings and illegal migration.
Fraud
United States of America dollar Counterfeiting
Commodity counterfeiting
money laundering
The scale and the typology of the crime phenomena vary within African
according to the country and the region. Therefore it is necessary to take into
account these variations at a regional and international level, when establishing
a coordinated response both for preventative and repressive actions.
People had resorted to the use passwords technology and global events has
significant impact on the world today. A more interactive and virtual society have
emerged through the use of the internet and as a result, has exposed
individuals and business to a host of security issues . Because of this securing
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and protecting valuable data and our identity have become areas of great
concern and cannot be ignored.
Clearly it has become critical in today’s environment to implement ways to
increase security levels. Maintaining and managing access while protecting both
the user’s identity and the computer’s data and systems have become
increasingly difficult .However no security is hundred percent efficient. But
nevertheless its my belief that integrated improved security system to compact
crime using biometrics will emerge as one of the most important aspects in
combating crime in today’s world.
Clearly it has become critical in today’s environment to implement ways to
increase security levels. Manufacturing and managing actors while protecting
both the user’s identity and the computer’s data and system has become
increasingly difficult.
However no security is hundred percent efficient. But nevertheless, its my belief
that improve security system to combat crime using biometrics is one of the
most important aspects in combating crime, in today’s world.
Surprisingly, extremely little research has been considered, the numerous
technical problems associated with the improved algorithms used in the matching
process either in non- biometric or in the biometrics.
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1.2 Aims and objectives
The aim of this research is to investigate the appropriateness of integrated
improved security system techniques in combating crime using the biometrics. It
is also the aim of this study to improve the algorithms used in the matching
process. To explore crime campaign awareness as other form of security
techniques. To evaluate the appropriatness and to test and analyze the
effectiveness of the integrated improved security system approach to combat
crime using the biometric.
1.3 Scope of the study
The importance of integrated improved security system techniques in combating
crime brought me to one of the aims of my study to improve the algorithm in the
matching process.
It is also in this research that the researcher will focus on other security system
techniques that need to be outlined in combating crime such as crime campaign
awareness. Since one of the requirements is to make people aware of the crimes
they commit on daily basis.
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1.4 Research Problem
To investigate the effect or applicability of the integrated improved security
system techniques used in combating crime in the matching process.
1.5 Justification of the study
The security system research community has focused primary on the use of
passwords and identity cards to combat crime separately. However it is my belief
that many of the complex, large scale crimes committed today may require the
integrated improved security system techniques approach. This ideal security
system techniques to handle such complex crimes will be one in which criminals
will find it difficult to get away with crime.
On the other hand, one of the requirements of the following security systems
technologies is to reduce crime and people are aware of it using the crime
campaigns awareness.
1.6 Overview of the Thesis
The structure of the rest of the report will be as follows: in chapter 2 we discuss
the major principles of the biometrics and the major research that has been
carried out in the area. In this chapter we look at the important terms used in
biometrics. In chapter 3 we discuss the review and analysis of the algorithm
used. Chapter 4 describes the conceptual assignment of the proposed algorithm
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for the fingerprint authentication. The data simulation is dealt with in chapter 5.
The testing and evaluation of the efficiency of the algorithm are dealt with in
chapter 6. Chapter 7, deals with the conclusion and evaluation of our findings
and the suggestions for future works found in the chapter.
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CHAPTER 2
LITERATURE REVIEW
2.1 Introduction to review on Biometrics.
The biggest challenges facing the Harare international Airport today is
confirming the true identity of a person. However there are several
identification verification schemes that exist today but the most accurate
identification schemes are in the area of Biometrics. The reason is that
physiological characteristics of a person cannot be used by another person.
It follows that the identity cards can be used by another person if lost or
stolen. Furthermore the matching process is made difficult if the person is aging.
Even if there is the recent and best algorithm it cannot detect the identity
of the person in the identity area. Though the identity cards can be changed
regularly but the problem is not solved.
Consider an example of an ATM card. The person wishes to use the ATM
card, is required to enter in a personal identification number (PIN) in order
to begin the transaction(s). The type of identification verification is given by
what the person has on the card. The fact is that the person knows the PIN.
But looking at the current situation there may be a potential problem to the
ATM scheme given above. There are various scenarios, the card could be
stolen. It is difficult for the thief to use the ATM card unless she / he knew
the PIN, But let us remember that the PIN is vulnerable to theft especially
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if someone is looking over the shoulder while entering the PIN number.
This example shows that crime is difficult to control. So using Biometrics such
as fingerprints alone it is difficult to eradicate crime. So this suggest the
importance of Biometrics should be used with another method to combat
crime. The appropriate technique is the crime campaign awareness. The
individual is made known to him/her before the commission of the crime.
Then those who did not understand the crime campaign awareness are
then detected by an improved algorithm in the matching process. So these
campaigns must be at different forums such as, the radio, the media, and
by the police officers at the National airport of Zimbabwe.
These different Biometrics characteristics are used for identification. The
domain include fingerprints, hand geometry, retina and iris patterns, facial
geometry, signatures and voice recognition. Now the current situation has
demonstrated that Biometrics are preferred over the traditional methods
such as the passwords and the identity cards. The main reason had been
mentioned earlier in their thesis. The interesting thing in dealing with
Biometrics identification is that, the individual to be identified is required to
physically be present at the point of identification and furthermore the
identification based on biometric techniques does not depend on the user to
remember a password or carry a token [1].
Two distinct function for biometric derive include that to prove you are who
you say you are and to prove you are not who you say you are not. The
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main purpose of the first function is to prevent the use of a single identity by
multiple people. It assist for a possible criminals to use the National Airport
to go to other countries for fear of arrest. Now it is important for the
biometric device be able to differentiate between a live biometric presented
to the scanner i.e. a real finger or a spoofed biometric trying to fool the
scanner.
This thesis will concentrate on improving the algorithm in the matching stage
.The other mentioned function is used to prevent the use of multiple identities by
a single person [1].Then must be ensured that the biometric system either
automatically cross checks the enrolled characteristics for duplicate and
utterance does not allow a person to register their biometrical i.e fingerprint under
two different names.[1]
Crime campaigns awareness must be used to sensitive individuals on the crime.
Now to provide improved security biometrics could be used in addition. This
helps reduce the number of people committing crime. The Biometrics has been
around for many years. The French anthropologist Alphnse Bertillon devised the
first widely accepted scientific method of Biometric identification in 1870 in
fingerprint aunthentication. . The Bertillon system Bertillonage or anthropometry
was not based on fingerprinting but rather relied on a systematic combination of
physical measurement .The measurements included measurements of the skull
width foot length and the length of the left middle finger combined within hair
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colour, eye colour as well as face and profile pictures. The next thirty years
Bertillonage was the primary method of biometric identification [2] The other
example of biometric in practice was a form of fingerprinting being used in China
in the 14th century as reported by Joao de Barros , he wrote that the Chinese
merchants were stamping children’s palm prints and footprints on paper with ink
to distinguish the young children from one another [3] Now the problem of these
identification is the matching process You find that after a certain period the child
in old cannot match the previous foot. This is the reason why improving the
material is of special importance.
The fingerprints are unique to each individual and each individual. Has his / her
pattern in the fingerprints. The police in the developed countries have
successfully used Biometrics to capture criminals and also finding missing
children. The fingerprint records the patterns found on a fingerprint. The
pattern matching is the one I feel need to be improved [4].
The fingerprint serve to reveal an individual’s true identity. This methods as
means of identification has been helpful. The idea is that fingerprints are
unique and has not been any type of pattern duplication by two different
people. The other point is that each individual has always his/her
fingerprints. The problem of stolen and cost in covered. The uniqueness
applies to the identical twins, triplets, quadruplets and the quintuplets
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furthermore any type of burn (superficial), abrasions, or cuts do not affect
the ridge structure, and hence the fingerprint pattern is unaffected. [5]
The hand geometry his/her analysing and measuring the shape of the
hand. The good thing is that it is relating easy to use. Since it is easy in
integration into other systems and process coupled with easy of use, then
makes it the fundamental step for many biometric projects. The problem is
that the hand geometry could be changed by smashing it with a hammer.
[3]
There have been six different hand- scanning products developed over this
span, including some of the most commercially successfully biometrics to
date [6]. The hand geometry biometric is by far less accurate than other
biometric methods. San Francisco International Airport, the USA’s fifth largest
airport has been using the hand geometry based systems to authenticate
the employees for almost 10 years [6].
The U.S federal Bureau of Prisons uses the hand geometry to identify
convents of its prisoners, staff and visitors [6]. Once the individual enters the
system the hand must be scanned [2]. The information is entered into a
database and the individual is issued a magnetic swipe card he/she carries
all the time [4].
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The retina-based Biometrics includes analyzing the layers of blood vessels
situated at the back of the eye. It uses the low intensity light source through an
optical computer to scan the unique patterns of the retina[7].
The method has limitations if the individual wears glasses. The other
problem is if the individual in having a close contact with retinal reading
device. The other problem is that if the individual has an eye disease such as
cataracts. Unfortunately this type of biometric is not accepted by many
users[3].
The retina scan becomes one of the oldest biometrics as 1930’s research
suggested that the patterns of board vessels on the bank of the human
eye were unique to each individual. The First retina scan device made for
commercial use was in 1984 [8].
The Iris based biometric includes analysing features found in the coloured
ring of tissue that surrounds the pupil [7] . The biometric has the potential
for higher than average template matching performance [7]. The idea of
using iris patterns for personal identification was originally proposed in
1936 by ophthamologist Frank Burch. In 1980 the idea had already
appeared in James Bond films. This demonstrates that it remained
schemes function and conjectures. By 1987 two other opthalmogists Aran
Jafis and Leonard Flom, patented the idea. In 1989 John Daugman was
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asked to create algorithms for the iris recognition [7]. The algorithms which
were patented in 1994 and are owned by Iridian technologies [7]. So the
issue of algorithm in the biometrics is of great significance.
In 1999, EyeTicket Corporation introduced Jetstream for passenger
processing including the airline. The method was used at the check in and
boarding , passport and visa control. The EyeTicket’s Jetstream and
EyePass programs operating at Charlotte Douglas International airport,
USA, at Heathrow airport, UK and elsewhere have accumulated in excess
of 400 000 transactions with 100% accuracy, no false identification , and no
security breaches [7]. The facial recognitions considers analysing the facial
characteristics as the case in the overall facial structure which includes the
distance between the eye, nose, mouth and jaw edges. The methods
worked together with digital video camera that captures the images of the
face . The methods had been widely used. The technique recorded
potential threats and so far has been improved in high-level usage. The
method of facial recognition started late 1980. The method was commercially
available systems were made in the 1990s [9].
However many peoples first heard about the facial recognition after
September 11th 2001. The football fans were introduced to it at the super
Bowl several years before the September 11th 2001.[10].
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The biometric signature verification is beyond the visual signature
comparison in its analysis of the way a user signs his/her home.[11].
However there are signing feature such as speed, velocity and pressure are
important as the finished signature verification devices are reasonably
accurate in operation. Though every person has a unique signature but that
signature is still vulnerable to duplication by thieves. The common
phenomenon is that if, one person tries to “forge” a signature he/she will
study the victim’s signature and practice that style of writing. Remember that
the attacker needs to know the variables such as speed, velocity and
pressure prior to attempting to forge a biometric signature [12]. The method
used to was simply working at two or more samples of a person’s
signature to see if they matched . This was called signature verification.
The method is widely used in Zimbabwe in the banks. Now the use of
digital signature verification is done by comparing the movement of how
one signs his/ her name.
The voice authentication considers allowing the user to use his/her voice
as an input device to the system. The voice commands the computers
began with application that were trained by the user to recognise certain
words that were spoken that the user can speak to a word processor
instead of actually typing the words out [13]. The problem with this
method is that poor quality and ambient noise can affect verification .
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However certain voice-scan technologies are resistant to imposter attacks
to a lesser degree than finger scan systems [14].
Biometrics has long been used in the identification beginning with the early use
of finger prints with the new technology will then be able to use devices that use
curve accurate in the biometrics [3]
2.2 Advantages and disadvantages of Biometrics
Currently biometrics offer much security but as compared to a Pin or a password
.The biometrics holds a set of advantages and disadvantages in general. The
table below indicates the properties [15]
Table 1.
Advantages Disadvantages
Positive Identification Public acceptance
You can’t lose, forget or share your
biometrics information.
Legal issues
A biometrics template is unique to
the individual for whom it is created.
Possible increase in hardware cost to
current systems
Rapid identifications/authentication May require large amounts of storage.
Costs in general are decreasing Privacy concerns
It is clear that the advantages outweigh the disadvantages. The reason is that
the main aim is to obtain positive identification [7], it is difficulty to falsify a
biometric trail of an authorized user, biometric (e.g. face or finger print). A good
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example is that finger prints are left in a wide variety of places in a given day
such as at homes and offices.
Individual has his/her biometric traits put into a template for later identification/
verification it becomes a unique template. The major problem is public
acceptance when implementing a new system or methods by which one abides
[16] The issue is that the public does not accept it then the fingerprint would be
difficult to implement successfully because it would not be used. Currently there
is a long list of legal resources that fingerprints imposes. The issues is that legal
issue are also taken into consideration in this study [17]
The hardware costs definitely increases and that becomes a constraint for any
agency or enterprise for to use fingerprint as a means for identification/
authentication. So the cost of the dynamic technology always become an issue.
The other thing is the storage allocation of biometrics templates also increase.
The table below indicate the fingerprint with the technology [18].
Table 2.
Technology Advantage Disadvantage
Fingerprint scanning/
matching
- Inexpensive
- Very secure
- physical contact to a
general scanning device
may spread germs
The fingerprint watching offers a very secure means of identification in an
inexpensive way [19] The draw back in that there is contact with general a
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scanning device that may propagate germs. So these is cost a in offering
antibacterial cleansing solution before and after each individual means his/her
fingerprint may reduce the problem [20]
2.3 The properties of biometrics
The automatic capturing (ie enrollment or authentication) of biometrics sample
data and comparison (ie matching with previously stored characteristics or
normative data requires the following properties of biometrics characteristics.
Invariance: Biometric properties should be constant over a long period of
time ie fingerprints taken during the issuing of national identify cards. Once
this is done it eliminate the need for constant updating of the template that are
stored in the system. The finger print is constant throughout an individual’s life
time as compared to facial characteristics which may change normal due to
aging .
Measurability and Timeliness: The individual properties must be able to be
automatically matched to an expected norm. The fingerprint sample should be
suitable for captured without wasting time which is important, for continuous
authentication and other complications because of the use of a technique
which will provide near real –time verification. The airport fingerprint
verification system needs to be able to capture fleeing criminals from or to
Zimbabwe.
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Singularity: The fingerprint characteristics should have sufficient unique
properties in order to differentiate one individual from another . The case is
true for fingerprints.
Reducibility: The stored data should be able to being reduced to a size that
is easy to handle but impossible to duplicate. The characteristic is important
especially when dealing with communicating the finger print data across
secure chancels (ie from verification device to the controller of the results
which may be in a remote area.)
Reliability. The fingerprint matching technique should ensure high reliability
and integrity. The airport matching system need to be reliable since, it would
be costly to have a system that does not provide consistent result.
Privacy : The fingerprint technique should ensure the privacy of the person
using the system so that their privacy is not being violated in any way [21]
2.4 Fingerprint Matching
Every individual posses a unique fingerprint from any other person. So the
fingerprint verification are based on two basic properties, the invariant and
singularity. The fingerprint verification has been used for a long time and
routinely used in forensic laboratories and identification units all around the
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worlds. The fingerprint evidence has also been accepted in courts of law for
nearly a century [7]
The whole world is familiar with fingerprint verification methods. The universality
makes this tactic have a high user acceptance rate. My experience was to use a
fingerprint scanner while applying for the new National identity card at Market
Square in Harare. The device was easy to use and the identity card was issued
to me without delay.
The step that followed was enrolling into the system, but then avoided the
matching process since I am not a criminal. I used, an optical scanner to get the
identify card. Also checked the monitors to free the resulting template.
Finger patterns can be represented by a large number of features [31]. The
common pattern including the overall ridge flow pattern, frequency, location and
position of singular points.
2.5 Fingerprint Limitations
The fingerprint matching method contains influences that may affect the outcome
of the verification process some of the constraints are [13]
Fingernail growth may have an effect on how firmly the user is able to
place his/her finger on the matching device. It may result in inaccurate
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results from the device as the user may be rejected by the system. This
covers the use of artificial nails that the user may apply to real fingernail.
Fingerprint fitness also have an effect on how the devices is able to pick
up details of the fingerprint. On this it depends on how well the depth and
the spacing of ridges are on the users finger. The constraint is controlled
by the user so proper enrollment from the beginning needs to be done.
Then also a proper placement of the finger on the matching device at the
time of verification, most common is that there is a fingerprint- matching
device that alleviate the problem by offering a sensitive “’touching area” for
the users.
The condition of the fingerprint also have an effect on the outcome of the
matching device because the user may have dry cracked, or damp
fingers. The issue is that suppose user has dry, cracked, or dump fingers
at the time of enrollment at the time of verification the matching algorithm
may not be sensitive to compensate for the differences . The other
obstacle is the scars and/ or scratches on the fingertips of the user. The
scars and scratches depending on the location obstruct some important
properties of the fingerprint that the matching device is seeking for to
extract. Some cases the matching device may be able to use to scar on
the fingertip as part of the properties to be extracted .
Temperature of the user’s finger or hand. It has been noted that the
temperature cause inaccurate results from the matching device. Personal
in June 2010 the matching device continuously reject me in the morning
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because they fingertips are cold. The matching device does liveness tests
based on temperature of the finger rather than on a pulse on the
fingertips.
2.6 Fingerprint matching techniques
The techniques/methods applied to gather fingerprint information has changed
greatly over the years. Sophisticated fingerprint matching methods have
emerged since the beginning of this techniques of verification. Some
complicated techniques available are [22]
Optical sensors with CCD or cmos camera. The fingerprint is placed or
pushed on a plate and is eliminated by a LED light source. Then through a
prism and a system of lenses, the image in projected on a camera. The
frame grabber technique are used and the image in stored and ready for
analysis
Ultrasonic sensors: Using ultrasonic sensor, a scan of the fingerprint with a
resolution of about 500 dots per inch in possible.
The technique in able to offer templates which are full of useful detail of finger
print information.
Electronic field sensor: The method creates an electric field with which an
array of pixels can measure variations in the election field that are caused by
the ridges and valleys in the fingerprint
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Capacitive sensors: The method is similar to electronic field sensor except
that when the finger in placed on the sensor an array pixels measures the
variations in capacity between the valleys and the ridges of the fingerprint
Temperature Sensors: The method makes distinction between the
temperature of the ridges and the temperature on the valley on the fingerprint.
A temperature match can be taken by simply swiping the finger over the
sensor.
The methods seem very advanced and accurate. Now it is still possible that a
desperate attacker may attempt to spoof a legitimate user by creating fake
fingers. The fake fingers can be made both by the cooperative of the legitimate
user (ie for testing methods) or without the cooperation of the legitimate use by
lifting a fingerprint off a keyboard or coffee mug. The traces of fingerprint are
known as latent fingerprints. Tsutomu Matsumoto a Japanese cryptographer has
discovered a means to fool many of the commercial fingerprint matchers
available using common ingredients [23].
One of Matshumoto’s more interesting experiments involves latent fingerprint.
He takes a fingerprint left on a piece of glass enhances it with a cyanoacrylate
adhesive and photographs it with a digital camera . Then using photoshop he
improves the contrast and print the fingerprint onto a transparency sheet. Then
he takes a photo sensitive printed circuit board (PCB) and uses the finger print
transparency to etch the finger print into the copper, making it three dimensional.
34
Finally he makes a gelatin finger using the print on the (PCB) This fools
fingerprint detectors about 80% of the time [17]
35
CHAPTER 3
REVIEW AND ANALYSIS OF EXISTING ALGORITHM
3.1 Minutia Matching
Given a diverse set of fingerprints of criminals in the criminal investigations
Department at the National Airport of Zimbabwe it is difficult to make the correct
match. The sound decision making will to an increasing extend rely on the
matching algorithm [27]
Matching algorithms systems does not only rely on the patterns of the finger
print such as the arch pattern, the loop patterns and the whorl pattern [28] The
figure below show the three patterns.
The arch Pattern The loop pattern The whorl Pattern
Figure 1
Even though some algorithms had looked to the major minutia features of finger
print ridges are: ridge ending , bifurcation and short ridge (or dot). The ridge
ending is the point at which a ridge terminates. Bifurcations are points at which
36
a single ridge splits into two ridges. Short ridges (or dots) are ridges which are
significantly shorter than the average ridge length on the fingerprint. According
to the previous algorithms these are characteristics which were well considered
[28]
Therefore we aim at exploiting matching algorithm which integrate the minutiae
and the patterns for the authentication of the finger print. The reason is no two
fingers have been shown to be identical [30]
Our research effort in the matching algorithm focuses on ensuring that all the
seven characteristics of the fingerprint are incorporated in the matching
algorithm. Our research should result in applications that will make it easy for
users unfamiliar with matching algorithm system to detect more criminals.
This chapter sees to give details on the matching algorithm of the finger prints.
Now to understand better the matching algorithm it is imperative to look at other
applied matching algorithms. Attention will be made to the existing matching
algorithms.
3.2 Existing Matching algorithms.
The following are some of the various matching algorithms that exist.The
following list is just a few of the existing matching algorithms which make use of
the minutae matching.
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3.3 Patterns based (or image- based) algorithms.
The pattern based algorithms compared the basic fingerprint patterns (arch,
whorl and loop) between previously stored template and a candidate fingerprint.
This requires that the images be aligned in the same orientation. The algorithm
finds a central point in the central fingerprint image and centers on that. The
candidate fingerprint image is graphically compared with the template that
determines the degree to which they match [31].
The above method uses the furthering fingerprint sensors. A finger sensor in an
electronic devise used to capture a digital image of the fingerprint pattern. The
captured image in called a live scan. The live scan is digitally processed to
create a template. A biometric template (collection of extracted features) which is
stored and used for matching for authentication purposes [31]
3.4 Optical
Optical fingerprint imaging involves capturing a digital image of the print using
visible light. The type of sensor is in essence a specialized digital camera. The
top layers of the sensor where the finger is placed is known as the touch surface.
Beneath this a layer is a light emitting phosphor layers which illuminates the
surface of the finger. The light reflected from the fingers passes through the
phosphor layer to an array of solid state pixels (a change - coupled device )
which captures a visual image of the fingerprint. The scratched or dirty touch
surface can cause a bad image of the finger print.[31]
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3.5 Disadvantage
The disadvantage of this type of senor is fact that the imaging capabilities are
affected by the quality of skin of the finger, The case is that a dirty or marked
finger is difficult to image properly . Then it is possible for an individual to erode
the outer layers of skin on the fingerprints to the point where the fingerprint in no
longer visible. This can be easily fooled by an image of a finger print if not
coupled with a live finger’ detectors. However, unlike capacitive sensors, this
sensors technology is not susceptible to electrostatic discharge damage [31]
3.6 Ultrasonic
Ultrasonic sensor makes use of the principles of medical ultrasonograph in order
to create usual images of the fingerprint. Unlike optima imaging , ultrasonic
sensor use very high frequenting sound waves to penetrate the epidermal layer
of skin. The sound waves are generated using piezoelectronic transducers and
reflected energy is also measured using piezoelectric materials. Then since the
dermal skin layer exhibits the same characteristics pattern of the fingerprint the
replaced waves measurements can be used to form an image of the finger print.
It eliminates the need for clean undamaged epidermal skin and a clean sensing
surface.
39
3.7 Capacitance
Capacitance sensors utilize the principle associated with capacitance in order to
further fingerprint images. The two equation used in this type of imagining are:-
C = Q/V
C = ξ0 ξr A/d
Where
C is the capacitance in farad
Q is the charge in coulombs
V is the potential in volts
ξ0 Is permittivity of free space, measured in farad per metre.
ξr is the dielectric constant of the insulates used.
A is the area of each plane electrode measured in square metres.
d is the separation between the electrodes measured in metres.
The method of imaging the sensor array pixels each act as the plate of a parallel-
plate capacitors, the dermal layer (which is electrically conductive) acts as the
other plate, and the non-conductive epidermal layer acts as a dielectric.
40
3.8 Passive Capacitance
The passive capacitance sensor uses the principle image of the fingerprint on
the dermal layer of skin . Each sensor pixel is used to measure the capacitance
at that point of the way . The capacitance varies between the valleys of the finger
print due to the fact that the volume between the dermal layer and the sensing
element in valleys contains an air gap. The dielectric of the epidermis and the
area of the sensing elements are known valid the measurements of capacitance
values. Then they are used to distinguish between finger print ridges and valleys.
[31]
3.9 Active Capacitance
The active capacitance sensor uses a changing cycle to apply a voltage to the
skin before measurement takes place. The application of voltage charges the
effective capacitor. The electric field between the finger and sensor follows the
pattern of the ridges in the dermal skin layer. On the discharge cycle the voltage
across the dermal layer and the sensing element is compared against a
reference voltage in order to calculate the capacitance. The distance values are
then calculated mathematically using the above equations and used to form an
image of the fingerprint. The active capacitance sensors measure the ridge
patterns of the dermal layers like the ultrasonic method. It also eliminates the
need for clean undamaged epidermal skin and a clean sensing surface.
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3.10 Minutia Matching Algorithm
This method uses for each detected minutia uses for each detected minutia ,
then realizes the following parameters.
1. x and y coordinates of the fingerprint point.
2. The orientation which is defined as the local ridge orientation of the
associated ridge.
3. The type of the fingerprint point that is whether the minutia is ridge ending
or ridge bifurcation.
4. The associated ridge is represented by points sampled at the average
inter- ridge distance along the ridge linked with the corresponding
fingerprint point [32]
3.11 Processes in existing matching algorithm
The existing algorithm is based on the following 3 steps.
1. There is live presentation of the finger print to the scanning machine
2. The template is formed.
3. The matching process is done.
We refer to the matching as the pattern based algorithm. The reason in that the
matching algorithm is biased toward the three patterns (i) arch (ii) loop and whorl
with little consideration on the minutia of the fingerprint. The matching algorithm
continuously does the matching in this category.
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3.12 Disadvantages of the existing system.
1. The matching algorithm did not realize all the principle of minutiae such
as
- ridge ending
- bifurcation
- Lake of enclosure
- Short ridge
- Dot
- Spur
- Crossover or bridge
The matching algorithm used the Cartesian Coordinates.
3.13 Architecture of the proposed algorithms.
Now looking at the nature and the operations of the existing system, the users of
the previous algorithms are one who need the most recent matching algorithm.
At this point depending on the nature or the classes of the users the proposed
matching algorithm has to provide a complete service to the decision makers
(Users).
43
3.14 Operations of the proposed matching algorithm
The proposed solution will be to create the matching algorithm that uses the
polar coordinates and uses and the minutiae of the fingerprint. The matching
Public Class BIOMETRICS Dim cnt, cnt1, cnt2, pecnt, n As Integer Dim pathf, thestr, filestr As String Dim doq, dop As Boolean
Private Sub BtnExit_Click(ByVal sender As System.Object, ByVal e AsSystem.EventArgs) Handles BtnExit.Click End End Sub
Private Sub BtnAuthenticate_Click(ByVal sender As System.Object, ByVal e As System.EventArgs) Handles BtnAuthenticate.Click Dim sr As New StreamReader(pathf) PicFingerPrint.Left = 74 PicDataBase.Top = 176 PicResult.Left = 423 cnt = 0 cnt1 = 0 cnt2 = 0 doq = False dop = False TmrQ.Enabled = False TmrTemp.Enabled = False
TxtStringF.Text = sr.ReadToEnd
sr.Close() TmrAction.Enabled = True
End Sub
Private Sub TmrAction_Tick(ByVal sender As System.Object, ByVal e As System.EventArgs) Handles TmrAction.Tick Dim palpha, plen, p As Integer TextBox1.Text = cnt cnt = cnt + 1 Select Case cnt Case 1 LblAction.Text = " Finger Print Scanner, Scanning Image" PicFingerPrint.Left = PicFingerPrint.Left + 12 Case 2 PicFingerPrint.Left = PicFingerPrint.Left + 12
Case 3 PicFingerPrint.Left = PicFingerPrint.Left + 12
Case 4 PicFingerPrint.Left = PicFingerPrint.Left + 12 LblAction.Text = " Finger Print Image" Case 5 PicFingerPrint.Left = PicFingerPrint.Left + 12
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Case 6 PicFingerPrint.Left = PicFingerPrint.Left + 12
Case 7 PicFingerPrint.Left = PicFingerPrint.Left + 12
Case 8 PicFingerPrint.Left = PicFingerPrint.Left + 12
Case 9 PicFingerPrint.Left = PicFingerPrint.Left + 12
Case 10 PicFingerPrint.Left = PicFingerPrint.Left + 12
Case 11 PicFingerPrint.Left = PicFingerPrint.Left + 12
Case 12 PicFingerPrint.Left = PicFingerPrint.Left + 12
Case 13 PicFingerPrint.Left = PicFingerPrint.Left + 12
Case 14 PicFingerPrint.Left = PicFingerPrint.Left + 12
Case 15 PicFingerPrint.Left = PicFingerPrint.Left + 12
Case 16 PicFingerPrint.Left = PicFingerPrint.Left + 12
Case 17 PicFingerPrint.Left = PicFingerPrint.Left + 12
Case 18 PicFingerPrint.Left = PicFingerPrint.Left + 12
Case 19 PicFingerPrint.Left = PicFingerPrint.Left + 12
Case 20 PicFingerPrint.Left = PicFingerPrint.Left + 12 Case 21 PicFingerPrint.Left = PicFingerPrint.Left + 12 Case 22 PicFingerPrint.Left = PicFingerPrint.Left + 12 Case 23 PicFingerPrint.Left = PicFingerPrint.Left + 12 Case 24 PicFingerPrint.Left = PicFingerPrint.Left + 12 Case 25 PicFingerPrint.Left = PicFingerPrint.Left + 12 LblAction.Text = " Minutia Extraction "
84
Case 26 PicDataBase.Top = PicDataBase.Top + 12
Case 27 PicDataBase.Top = PicDataBase.Top + 12
Case 28 PicDataBase.Top = PicDataBase.Top + 12 LblAction.Text = "Minutia" Case 29 PicDataBase.Top = PicDataBase.Top + 12
Case 30 PicDataBase.Top = PicDataBase.Top + 12
Case 31 PicDataBase.Top = PicDataBase.Top + 12
Case 32 PicDataBase.Top = PicDataBase.Top + 12
Case 33 PicDataBase.Top = PicDataBase.Top + 12
Case 34 PicDataBase.Top = PicDataBase.Top + 12
Case 35 PicDataBase.Top = PicDataBase.Top + 12
Case 36 PicDataBase.Top = PicDataBase.Top + 12
Case 37 PicDataBase.Top = PicDataBase.Top + 12
Case 39 PicDataBase.Top = PicDataBase.Top + 12
Case 40 PicDataBase.Top = PicDataBase.Top + 12 LblAction.Text = "Minutia Matching" Case 41 PicDataBase.Top = PicDataBase.Top + 12
Case 42
Case 43 Case 44 Case 45 Case 46 Case 47 Case 48 Case 49 Case 50
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LblAction.Text = "Minutia Matching" & vbNewLine & vbNewLine & "1. Matching the ridge associated with each input minutia against the ridge asociated with each template minutia and align the two patterns according to the matching result" TmrTemp.Enabled = True plen = TxtStringF.TextLength For p = 0 To plen - 1 thestr = Strings.Mid(TxtStringF.Text, p + 1, 1) If thestr >= "0" And thestr <= "9" Then palpha = palpha + 1 End If If thestr >= "A" And thestr <= "Z" Then palpha = palpha + 1 End If If thestr >= "a" And thestr <= "z" Then palpha = palpha + 1 End If Next txtp.Text = palpha Case 51 Case 52 Case 53 Case 54 Case 55 Case 56 Case 57 Case 59 Case 60
LblAction.Text = "Minutia Matching" & vbNewLine & vbNewLine & "2. Convert the representation of template and input minutiate into the polar coodinate representation with respect to the corresponding minutia on which alignment is performed and represent them as two symbolic strings by concatenating each minuta in an increasing order of radial angles: " TmrTemp.Enabled = True TxtImg.Text = TxtStringF.TextLength / txtp.Text
Case 61 Case 62 Case 63 Case 64 Case 65 Case 66 Case 67 Case 68 txtq.Text = LstQ.Items.Item(0) TxtTemplate.Text = LstTempMin.Items.Item(0) Case 69 Case 70 LblAction.Text = "Minutia Matching" & vbNewLine & vbNewLine & "3. Matching the resulting strings Pp and Qq with a modified dynamic-programming algorithm decribed below to find the 'edit distance' between Pp and Qq." TmrTemp.Enabled = True
86
Case 71 TmrQ.Enabled = True Case 72 Case 73 Case 74 Case 75 Case 76 Case 77 Case 78 Case 79 Case 80 LblAction.Text = "Minutia Matching" & vbNewLine & vbNewLine & "4. Compute the matching score of the template and input minutiae a the minimum 'edit distance'." & vbNewLine & "The max and min values of the matching score are 100 and 1, respectively. The former value indicates a perfect match, while the later value indicates no match at all." TmrTemp.Enabled = True If TxtImg.Text >= TxtTemplate.Text Then txtMatching.Text = (100 * TxtTemplate.Text ^ 2) / (TxtImg.Text ^ 2) Else txtMatching.Text = (100 * TxtImg.Text ^ 2) / (TxtTemplate.Text ^ 2) End If
If txtMatching.Text <= pecnt Then PicResult.Visible = False PicNo.Visible = True Else PicResult.Visible = True PicNo.Visible = False End If PicNo.Left = PicResult.Left End If
If cnt >= 106 Then TmrAction.Enabled = False cnt = 0 End If End Sub
Private Sub BtnBrowesPrint_Click(ByVal sender As System.Object, ByVal e As System.EventArgs) Handles BtnBrowesPrint.Click 'TmrAction.Enabled = True OpenFileDialog1.InitialDirectory = "BIOMETRICS" 'OpenFileDialog1.ShowDialog() If OpenFileDialog1.ShowDialog = Windows.Forms.DialogResult.OK Then LstFingerP.Items.Add(My.Computer.FileSystem.GetName(OpenFileDialog1.FileName)) LstPath.Items.Add(OpenFileDialog1.FileName) End If
End Sub
Private Sub BIOMETRICS_Load(ByVal sender As System.Object, ByVal e As System.EventArgs) Handles MyBase.Load For n = 0 To 100 ComboBox1.Items.Add(n) Next ComboBox1.SelectedIndex = 75 TxtToReport.Text = "" TxtToReport.Text = TxtToReport.Text & vbTab & vbTab & "Normalised Matching Score" & vbNewLine & "----------------------------------------------------------------------------------------------"
Private Sub PicComputer_Click(ByVal sender As System.Object, ByVale As System.EventArgs)
End Sub
Private Sub OpenFileDialog1_FileOk(ByVal sender As System.Object, ByVal e As System.ComponentModel.CancelEventArgs) HandlesOpenFileDialog1.FileOk
End Sub
Private Sub TmrTemp_Tick(ByVal sender As System.Object, ByVal e AsSystem.EventArgs) Handles TmrTemp.Tick cnt1 = cnt1 + 1 Select Case cnt1
Case 1
Case 2 PicTemp.BackgroundImage = ImageList1.Images.Item(cnt1)
Case 3 PicTemp.BackgroundImage = ImageList1.Images.Item(cnt1)
Case 4 PicTemp.BackgroundImage = ImageList1.Images.Item(cnt1)
Case 5 PicTemp.BackgroundImage = ImageList1.Images.Item(cnt1)
Case 6 PicTemp.BackgroundImage = ImageList1.Images.Item(cnt1)
Case 7 PicTemp.BackgroundImage = ImageList1.Images.Item(cnt1)
Case 8 PicTemp.BackgroundImage = ImageList1.Images.Item(cnt1)
Case 9 PicTemp.BackgroundImage = ImageList1.Images.Item(cnt1)
Case 10 PicTemp.BackgroundImage = ImageList1.Images.Item(cnt1)
Case 11 PicTemp.BackgroundImage = ImageList1.Images.Item(cnt1)
Case 12 PicTemp.BackgroundImage = ImageList1.Images.Item(cnt1)
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Case 13 PicTemp.BackgroundImage = ImageList1.Images.Item(cnt1)
Case 14 PicTemp.BackgroundImage = ImageList1.Images.Item(cnt1)
Case 15 PicTemp.BackgroundImage = ImageList1.Images.Item(cnt1)
Case 16 PicTemp.BackgroundImage = ImageList1.Images.Item(cnt1)
Case 17 PicTemp.BackgroundImage = ImageList1.Images.Item(cnt1)
Case 18
Case 19
Case 20 cnt1 = 0 TmrTemp.Enabled = False End Select End Sub
Private Sub BtnReport_Click(ByVal sender As System.Object, ByVal e As System.EventArgs) Handles BtnReport.Click Dim rd As New StreamReader("C:\BIOMETRICS\BIOMETRICS.xls") filestr = rd.ReadToEnd rd.Close() writetofile() Process.Start("C:\BIOMETRICS\BIOMETRICS.xls")
End Sub
Public Sub writetofile()
Dim sw As New StreamWriter("C:\BIOMETRICS\BIOMETRICS.xls") sw.WriteLine(filestr & Txtexcel.Text) sw.Close()
End Sub
Private Sub LstFingerP_SelectedIndexChanged(ByVal sender AsSystem.Object, ByVal e As System.EventArgs) HandlesLstFingerP.SelectedIndexChanged If LstFingerP.SelectedIndex >= 0 Then pathf = LstPath.Items.Item(LstFingerP.SelectedIndex) End If End Sub
Private Sub Button2_Click(ByVal sender As System.Object, ByVal e AsSystem.EventArgs) Handles Button2.Click
Private Sub Button1_Click(ByVal sender As System.Object, ByVal e AsSystem.EventArgs) Handles Button1.Click
Dim adtemp, PLEN, P, PALPHA As Integer
Dim sr1 As New StreamReader(pathf)
TxtStringF.Text = sr1.ReadToEnd
sr1.Close()
PLEN = TxtStringF.TextLength For P = 0 To TxtStringF.TextLength - 1
thestr = Strings.Mid(TxtStringF.Text, P + 1, 1) If thestr >= "0" And thestr <= "9" Then PALPHA = PALPHA + 1 End If If thestr >= "A" And thestr <= "Z" Then PALPHA = PALPHA + 1 End If If thestr >= "a" And thestr <= "z" Then PALPHA = PALPHA + 1 End If
Next If PALPHA > 0 Then adtemp = TxtStringF.TextLength / PALPHA
End If
LstQ.Items.Add(PALPHA) LstTempMin.Items.Add(adtemp) End Sub
Private Sub TmrQ_Tick(ByVal sender As System.Object, ByVal e AsSystem.EventArgs) Handles TmrQ.Tick Dim temp As Integer
If cnt2 >= LstQ.Items.Count - 1 Then TmrQ.Enabled = False 'cnt2 = 0 End If LstQ.SelectedIndex = cnt2 LstTempMin.SelectedIndex = cnt2
For temp = cnt2 To LstQ.Items.Count - 1
92
If txtq.Text / LstQ.Items.Item(cnt2) > txtq.Text / LstQ.Items.Item(temp) Then txtq.Text = LstQ.Items.Item(cnt2) TxtTemplate.Text = LstTempMin.Items.Item(cnt2) ElseIf txtq.Text / LstQ.Items.Item(cnt2) < txtq.Text / LstQ.Items.Item(temp) Then txtq.Text = LstQ.Items.Item(temp) TxtTemplate.Text = LstTempMin.Items.Item(temp) End If
Next
If txtq.Text = txtp.Text Then TmrQ.Enabled = False TmrTemp.Enabled = False ' cnt2 = 0 End If
cnt2 = cnt2 + 1
End Sub
Private Sub Tmrtemplate_Tick(ByVal sender As System.Object, ByVal e As System.EventArgs)
End Sub
Private Sub ComboBox1_SelectedIndexChanged(ByVal sender AsSystem.Object, ByVal e As System.EventArgs) HandlesComboBox1.SelectedIndexChanged pecnt = ComboBox1.Text End SubEnd Class
93
<Global.Microsoft.VisualBasic.CompilerServices.DesignerGenerated()> _Partial Class BIOMETRICS Inherits System.Windows.Forms.Form
'Form overrides dispose to clean up the component list. <System.Diagnostics.DebuggerNonUserCode()> _ Protected Overrides Sub Dispose(ByVal disposing As Boolean) If disposing AndAlso components IsNot Nothing Then components.Dispose() End If MyBase.Dispose(disposing) End Sub
'Required by the Windows Form Designer Private components As System.ComponentModel.IContainer
'NOTE: The following procedure is required by the Windows Form Designer 'It can be modified using the Windows Form Designer. 'Do not modify it using the code editor. <System.Diagnostics.DebuggerStepThrough()> _ Private Sub InitializeComponent() Me.components = New System.ComponentModel.Container Dim resources As System.ComponentModel.ComponentResourceManager = NewSystem.ComponentModel.ComponentResourceManager(GetType(BIOMETRICS)) Me.Label1 = New System.Windows.Forms.Label Me.GroupBox1 = New System.Windows.Forms.GroupBox Me.BtnExit = New System.Windows.Forms.Button Me.BtnReport = New System.Windows.Forms.Button
Me.BtnAuthenticate = New System.Windows.Forms.Button Me.BtnBrowesPrint = New System.Windows.Forms.Button Me.TmrAction = New System.Windows.Forms.Timer(Me.components) Me.OpenFileDialog1 = New System.Windows.Forms.OpenFileDialog Me.LblAction = New System.Windows.Forms.Label Me.GroupBox2 = New System.Windows.Forms.GroupBox Me.Label3 = New System.Windows.Forms.Label Me.Label6 = New System.Windows.Forms.Label Me.txtMatching = New System.Windows.Forms.TextBox Me.Label5 = New System.Windows.Forms.Label Me.TxtTemplate = New System.Windows.Forms.TextBox Me.Label4 = New System.Windows.Forms.Label Me.Label2 = New System.Windows.Forms.Label Me.TxtImg = New System.Windows.Forms.TextBox Me.txtq = New System.Windows.Forms.TextBox Me.txtp = New System.Windows.Forms.TextBox Me.LstFingerP = New System.Windows.Forms.ListBox Me.ImageList1 = NewSystem.Windows.Forms.ImageList(Me.components) Me.TmrTemp = New System.Windows.Forms.Timer(Me.components) Me.TxtToReport = New System.Windows.Forms.TextBox Me.TxtStringF = New System.Windows.Forms.TextBox Me.LstTempMin = New System.Windows.Forms.ListBox Me.Button1 = New System.Windows.Forms.Button Me.Button2 = New System.Windows.Forms.Button Me.LstQ = New System.Windows.Forms.ListBox Me.PicNo = New System.Windows.Forms.PictureBox
94
Me.PicTemp = New System.Windows.Forms.PictureBox Me.PicServer = New System.Windows.Forms.PictureBox Me.PictureBox1 = New System.Windows.Forms.PictureBox Me.PicDB = New System.Windows.Forms.PictureBox Me.PicResult = New System.Windows.Forms.PictureBox Me.PicDataBase = New System.Windows.Forms.PictureBox Me.PicFingerPrint = New System.Windows.Forms.PictureBox Me.PicComputer = New System.Windows.Forms.PictureBox Me.TmrQ = New System.Windows.Forms.Timer(Me.components) Me.LstPath = New System.Windows.Forms.ListBox Me.TextBox1 = New System.Windows.Forms.TextBox Me.ComboBox1 = New System.Windows.Forms.ComboBox Me.Txtexcel = New System.Windows.Forms.TextBox Me.GroupBox1.SuspendLayout() Me.GroupBox2.SuspendLayout() CType(Me.PicNo, System.ComponentModel.ISupportInitialize).BeginInit() CType(Me.PicTemp, System.ComponentModel.ISupportInitialize).BeginInit() CType(Me.PicServer, System.ComponentModel.ISupportInitialize).BeginInit() CType(Me.PictureBox1, System.ComponentModel.ISupportInitialize).BeginInit() CType(Me.PicDB, System.ComponentModel.ISupportInitialize).BeginInit() CType(Me.PicResult, System.ComponentModel.ISupportInitialize).BeginInit() CType(Me.PicDataBase, System.ComponentModel.ISupportInitialize).BeginInit() CType(Me.PicFingerPrint, System.ComponentModel.ISupportInitialize).BeginInit() CType(Me.PicComputer, System.ComponentModel.ISupportInitialize).BeginInit() Me.SuspendLayout() ' 'Label1 ' Me.Label1.AutoSize = True Me.Label1.Location = New System.Drawing.Point(37, 426) Me.Label1.Name = "Label1" Me.Label1.Size = New System.Drawing.Size(98, 13) Me.Label1.TabIndex = 4 Me.Label1.Text = "Finger Print Reader" ' 'GroupBox1 ' Me.GroupBox1.Controls.Add(Me.BtnExit) Me.GroupBox1.Controls.Add(Me.BtnReport) Me.GroupBox1.Controls.Add(Me.BtnAuthenticate) Me.GroupBox1.Controls.Add(Me.BtnBrowesPrint) Me.GroupBox1.Location = New System.Drawing.Point(12, 607) Me.GroupBox1.Name = "GroupBox1" Me.GroupBox1.Size = New System.Drawing.Size(947, 80) Me.GroupBox1.TabIndex = 6 Me.GroupBox1.TabStop = False ' 'BtnExit
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' Me.BtnExit.Font = New System.Drawing.Font("Microsoft Sans Serif", 12.0!, System.Drawing.FontStyle.Regular, System.Drawing.GraphicsUnit.Point, CType(0, Byte)) Me.BtnExit.Location = New System.Drawing.Point(788, 19) Me.BtnExit.Name = "BtnExit" Me.BtnExit.Size = New System.Drawing.Size(126, 44) Me.BtnExit.TabIndex = 3 Me.BtnExit.Text = "Exit" Me.BtnExit.UseVisualStyleBackColor = True ' 'BtnReport ' Me.BtnReport.Font = New System.Drawing.Font("Microsoft Sans Serif", 12.0!, System.Drawing.FontStyle.Regular, System.Drawing.GraphicsUnit.Point, CType(0, Byte)) Me.BtnReport.Location = New System.Drawing.Point(555, 18) Me.BtnReport.Name = "BtnReport" Me.BtnReport.Size = New System.Drawing.Size(126, 44) Me.BtnReport.TabIndex = 2 Me.BtnReport.Text = "View Report" Me.BtnReport.UseVisualStyleBackColor = True ' 'BtnAuthenticate ' Me.BtnAuthenticate.Font = New System.Drawing.Font("Microsoft Sans Serif", 12.0!, System.Drawing.FontStyle.Regular, System.Drawing.GraphicsUnit.Point, CType(0, Byte)) Me.BtnAuthenticate.Location = New System.Drawing.Point(298, 18) Me.BtnAuthenticate.Name = "BtnAuthenticate" Me.BtnAuthenticate.Size = New System.Drawing.Size(126, 44) Me.BtnAuthenticate.TabIndex = 1 Me.BtnAuthenticate.Text = "Authenticate" Me.BtnAuthenticate.UseVisualStyleBackColor = True ' 'BtnBrowesPrint ' Me.BtnBrowesPrint.Font = New System.Drawing.Font("Microsoft Sans Serif", 12.0!, System.Drawing.FontStyle.Regular, System.Drawing.GraphicsUnit.Point, CType(0, Byte)) Me.BtnBrowesPrint.Location = New System.Drawing.Point(34, 19) Me.BtnBrowesPrint.Name = "BtnBrowesPrint" Me.BtnBrowesPrint.Size = New System.Drawing.Size(126, 44) Me.BtnBrowesPrint.TabIndex = 0 Me.BtnBrowesPrint.Text = "Finger Print" Me.BtnBrowesPrint.UseVisualStyleBackColor = True ' 'TmrAction ' Me.TmrAction.Interval = 450 ' 'OpenFileDialog1 ' Me.OpenFileDialog1.FileName = "OpenFileDialog1" ' 'LblAction '