CONTRIBUTIONS TO BIOMETRIC RECOGNITION:
FINGERPRINT FOR IDENTITY VERIFICATION
By
Ebanehita Jude Esekhaigbe
A DISSERTATION
Submitted
to Cardiff Metropolitan University
in partial fulfilment of the requirements
for the degree of
BSc (Hons) Business Information Systems
2016
SUPERVISOR
DR. Jason Williams
ABSTRACT
Biometrics is an exceptional strategy used to recognize people by region organic describes.
As of late biometrics are showing up all over from home, schools, working environment and
banks. This identification method is rapidly replacing existing system, for example,
passwords since it offers a more elevated amount of security compared with existing strategy.
Fingerprint recognition has been effectively utilized as a part of law authorization and crime
scene investigation to identify suspects and exploited people for over a century. Recent
advances in automated fingerprint identification systems (AFIS), coupled with the developing
requirement for dependable individual recognition, have brought about an expanded
organization of AFIS in expansive applications, for example, border control, employment
background checks, secure office access, and client validation in tablets and cell phones.
In spite of the achievement of fingerprint recognition techniques in numerous large-scale and
differing individual recognizable proof applications, several challenging issues in fingerprint
recognition still need to be tended to. First and foremost, the determination and uniqueness of
fingerprints—the fundamental premises for fingerprint recognition stay as presumptions
instead of facts with strong scientific underpinnings. Though some studies have tended to the
uniqueness of fingerprints, there has been no deliberate study gave on the persistence of
fingerprints.
KEY WORDS: Automated Fingerprint Identification Systems (AFIS), DNA, FTIR
(Frustrated Total Internal Reflection), MSI, TBS, MFS-500, PC, ID cards, biometrics
verification, security systems, PIN number, recognition, behavioural, identification,
techniques, ridges, furrows, minutiae, forensic issues, enrolment, authentication, algorithm,
equations, corrugation, robust, probabilistic model, uniqueness, pores, endoscopy, illuminate,
enhancement, curvature surface, finger mask, orthogonal vectors, truncation, sensor.
ACKNOWLEDGMENTS
I consider myself a very fortunate person for having so many people to be thankful to. I am
most grateful to my supervisor, Dr. Jason Williams who hasn’t just been a supervisor but also
a perfect advisor to me. He was always supportive of my research, respected and determined
my research directions, and my ways of conducting research with great patience. I also want
to show great appreciation to my ex supervisor DR Lynne Norris Jones who supported me
from the start of my dissertation who was also helpful and supportive and been there for me
whenever I needed any help, sometimes even before I was aware that I needed help.
Moreover, I am also grateful to all my tutors from the start of my course especially DR. Steve
Marsh, DR. Hilary Berger, Jason Williams, Panicos Georghiade, Oatley Giles and the
International student welfare helper. Each and all of them helped me in one way or another
and I am grateful to all of them for being there for me. There were a few difficult and
frustrating moments for sure, but there was never a moment that I regretted coming here and
pursuing my BSc degree. I am very thankful for all the wonderful people I have met and for
everything I have learned.
I also would like to thank my Dad Major General Cecil Esekhaigbe and Mum Mrs Angela
Esekhaigbe for their Parental support showered on me all through my academic journey. My
love extends to my siblings Omotesele, Ose, Eki and Ewanlen for being so supportive of
everything, every decision, every step, and every journey. They have always been there for
me and I am so grateful. I grew up in a wonderful family, and it was not easy to be far from
them, but they were always very supportive and understanding.
And last but not least, I would like to thank all my friends for everything. I am very fortunate
to have friends to count on, laugh with and learn together. It is always wonderful to be with
them. Thank you to everyone who was a part of this journey. Your support made this
possible.
Table of Contents
CHAPTER 1 INTRODUCTION .............................................................................................. 6
1.2 Research Rationale: ............................................................................................................ 7
Chapter-2 LITERATURE REVIEW ........................................................................................ 9
2.1 Biometric from 1950 – 2014 ................................................................................................ 9
2.2 Biometric System Types: ................................................................................................... 11
2.3 Fingerprint Recognition System: Past to Present ................................................................ 13
2.4 Fingerprint Features and Detail: ........................................................................................ 13
2.5 Sensing Technology for Fingerprints: ................................................................................. 14
2.6 Fingerprints Detail: ........................................................................................................... 16
2.7 Fingerprint Acquisition Method: ....................................................................................... 17
2.7.1 Optical Method: ................................................................................................................... 17
2.7.2 Ultrasonic Method: .............................................................................................................. 18
2.7.3 Capacitance Method: ........................................................................................................... 19
2.8 Fingerprint Enhancement Techniques:............................................................................... 19
2.9 Fingerprint Matching Technique:....................................................................................... 24
2.9.1 Minutiae Based: ................................................................................................................... 25
2.9.2 Image Based: ........................................................................................................................ 26
2.9.3 Ridge Feature Base: ............................................................................................................. 27
2.9.4 Correlation based matching ................................................................................................. 28
2.9.5 Hybrid Methods ................................................................................................................... 29
2.10 Biometric Characteristics:................................................................................................ 29
2.11 Biometric Application: .................................................................................................... 30
2.12 Biometric Application Types: ........................................................................................... 30
2.12.1 Biometric System Model: ................................................................................................... 31
2.13 System Design Issues with Biometric System ................................................................... 33
CHAPTER-3: RESEARCH METHODOLOGY ......................................................................... 33
3.1 Introduction ..................................................................................................................... 33
3.2 Research Design: .............................................................................................................. 34
3.3 Explanatory Research Design ............................................................................................ 34
3.4 Validity and Reliability: ..................................................................................................... 34
3.5 Specification of the Research Model:................................................................................. 34
3.6 Biometric Cryptosystem: ................................................................................................... 35
3.7 Pre-processing: ................................................................................................................. 35
3.8 Sampling Normalization: ................................................................................................... 35
3.9 Segmentation of Hands: .................................................................................................... 36
3.10 Finger Extraction: ............................................................................................................ 37
3.11 Feature template Generation: ......................................................................................... 38
3.12 Matching Techniques: ..................................................................................................... 42
3.13 Experiments for Verification:........................................................................................... 44
CHAPTER 4- RESULTS AND DISCUSSION........................................................................... 45
4.1 Results and Discussion ...................................................................................................... 45
REFERENCES ................................................................................................................... 48
BIBLIOGRAPHY ............................................................................................................... 51
List of Tables
Table 1................................................................................................................................................... 17
Table 2................................................................................................................................................... 42
List of Figures
Figure 1: The six major fingerprint classes: (a) arch, (b) tented arch, (c) left loop, (d) right loop,
(e)whorl, and (f) twin-loop.................................................................................................................... 15
Figure 2: Examination of distinctive unique fingerprint impressions: (a) an inked rolled fingerprint
(from NIST 4 database); (b) an inked dab fingerprint (from NIST 4 database); (c) live-scan (dab)
fingerprint (captured with a scanner manufactured by Digital Bio ...................................................... 15
Figure 3: An even-symmetric Gabor filter: (a) Gabor filter tuned to 60 cycles/width and 00
orientation; (b) corresponding modulation transfer function (MTF) ................................................... 20
Figure 4: Fingerprint Enhancement Algorithm. © Academic Press ...................................................... 21
Figure 5:Examples of enhancement results; (a) and (c) are the input images; (b) and (d) show
enhanced recoverable regions superimposed on the corresponding input images. ........................... 22
Figure 6:Fingerprint Enhancement Results: (a) a poor quality fingerprint; (b) minutia extracted
without image enhancement; and (c) minutiae extracted after image enhancement ........................ 22
Figure 7:Two separate impressions of the same finger. In order to know the correspondence
between the minutiae of these two fingerprint pictures, all the minutiae must be absolutely
restricted and the disfigurements must be recuperated. .................................................................... 24
Figure 8:Difficulty in fingerprint matching. (a) and (b) have the same global configuration but are
images of two different fingers............................................................................................................. 25
Figure 9:Example of matched minutiae from a fingerprint pair (latent and rolled print). ................... 26
Figure 10: Ridges and automatically detected minutiae points in a fingerprint image. The core is
marked with a ×. ................................................................................................................................... 28
Figure 11: Sample size in X direction and Sample Size in Y direction ................................................... 36
Figure 12: Valley Location on a finger ................................................................................................... 39
Figure 13: Pixel attraction on a finger ................................................................................................... 39
Figure 14: Finger Mask .......................................................................................................................... 39
Figure 15: Z=0.001 ................................................................................................................................ 41
Figure 16: Z=0.002 ................................................................................................................................ 41
CHAPTER 1 INTRODUCTION
The traditional security systems offer security through PIN number or the passwords. These
two methods are the knowledge based methods whereas the other method that is token based
such as license, ID, passport are also highly prone to frauds as they can be the passwords can
be hacked, PIN code may be forgotten and ID cards and other tokens may be duplicated or
lost. It is essential to have a robust and reliable and fool-proof security system for
identification and verification. An accurate identification system is highly resistible to the
attacks and offer limited access. (Ashbourn, 2004)
Biometric security systems offer the most reliable personal security through various
techniques such as face recognition, finger recognition, Iris and retina recognition. Among all
the techniques, fingerprint recognition is considered most effective and reliable for personal
identification. The origin of word biometric is from Greek language and its literal meaning is
“life to measure.” Biometric system provide security by using the personal or behavioural
characteristics of human such as fingerprints, iris, veins, voice, typing rhythm or handwriting.
Biometric systems use the data that is non-intrusive and unique and time invariant for every
individual. (Jain, Ross & Prabhakar, 2004).
Biometric security systems are supported worldwide by its users and 70% consumers across
the globe uses this security system for identification. (Feng, 2010) And finger print
identification is most commonly used biometric technique in various organizations such as
banks, Public and private organizations, government agencies and healthcare providers. The
research conducted by Habib, Hameed and Shahrukh (2013) shows that 61% of the
organizations use biometric fingerprint recognition as a preferred security system worldwide.
Fingerprint is the pattern of varying unique features and every human being on earth is
believed to have different and unique and consists on various ridges and furrows. That is
why; fingerprints are used for investigations and identification. However, for identification
purpose minutiae plays a key role instead of ridge and furrow of fingers. (Perkins, 2001)
The benefits that fingerprint recognition provide the accuracy at the highest level, scope is
broad and it has now been used in almost all fields of life for security purpose such as ecommerce, physical access to any organization or agency, PC’s and other security devise. It
is very simple to use however, basically it is associated with the application in criminal or
forensic issues.
By replacing PINs, biometric techniques can potentially prevent unauthorized
access to or fraudulent use of the following: ATMs, Cellular phones, Smart cards,
Desktop PCs, Workstations, Computer networks PINs and passwords may be
forgotten and token-based identification methods such as passports and driver’s
licenses may be forged, stolen, or lost. Thus, biometric systems of identification
are enjoying a new interest. Various types of biometric systems are being used for
real-time identification.
1.2 Research Rationale:
In relation to the research question, this paper will cover the following aspects that are:
•
Development and characteristic of the biometric system and its application and issues
•
Fingerprint matching technique and matching algorithm
These two aspects are the core of this research paper to the point of suggesting a reliable and
robust system using fingerprints. Biometric systems provide security with accuracy and
authenticated system of identification than various other methods such as personal
identification number (PIN) password or other such methods. However, every technology has
some limitations and has to be considered during the application process. The biometric
system has implications and limitations too as according to Ashbourn (2004) a societal
change that is fundamental requires efforts to be understand and implemented along with its
implications.
The industry experts are of the view that biometric system are to be assessed before
deployment and in case of failure, human factors are the reason and need to be blamed.
(Kumar, 2009) While, some other experts are of the view that, practical application and
performance has been affected because of the performance metrics such as false non-match
and false match. The example can be taken of the biometric systems such as BioAPI and
Biometric Exchange File Format both these system were not adopted widely. (Ruud,
Jonathan, Nalini and Andrew, 2004) Because, in this era where security is the major concern
of the governments, a system is required that is extremely reliable and robust and offer fooproof security.
The approach of this paper is to study the biometric system for recommending a fingerprint
authentication system. As a result of it is the leading biometric technique and can be applied
commercially. Fingerprint scanners are also in demand because of low cost and high
performance.
1.3 Purpose of the Study
The aim of this research is threefold that is, to discuss the biometric system for
fingerprint identification technique, secondly, to study the fingerprint technique in
detail, that why fingerprints are considered more appropriate for personal
identification? What are the unique features and how they are extracted using a
biometric scanner? Thirdly, to analyze the matching technique of a biometric
system that, how a fingerprints are matched with accuracy to provide security.
The specific objective of this study is to:
1. Evaluate the verification and identification of biometric fingerprints.
2. Determine whether there is biometric accuracy of fingerprints of people over
time.
3. Assess the reliability of biometric fingerprints in forensics.
1.4 Scope of the Study
The academic scope of the study is the Contributions to Biometric Identification:
Fingerprint for Identity Verification.
1.5 Definition of Terms
1. Biometrics
Biometrics consists of methods for uniquely recognizing humans based upon one
or more intrinsic physical or behavioral traits.
2. Identification
This involves establishing a person's identity based only on biometric
measurements.
3. Verification
It involves confirming or denying a person's claimed identity
4. Fingerprint
It involves taking an image of a person's fingertips and records its characteristics
like
whorls, arches, and loops along with the patterns of ridges, furrows, and minutiae.
5. Contribution
Efforts of an object or activity in bringing about a result.
Chapter-2 LITERATURE REVIEW
In recent years renewed attention has been given to the research on biometric methods due to
the increased concern about the security. The governments have become proactive about the
security measures because of the issues such as terrorism. Among other things, these risks
have increased concern for the security of individuals and homes, personal possessions and
working environment must be protected to ensure safety. Various techniques have been
developed and implemented successfully for security and law enforcement. State of the art
techniques have been used in biometric methods and fingerprint recognition is the most
common and powerful technique. Biometric industry is developing new technologies and
bringing innovations to meet the increasing demands of biometric. (Tico, Immonen, Rämö,
Kuosmanen & Saarinen, 2001).
2.1 Biometric from 1950 – 2014
The term Biometric was derived from “bio” that is a Greek word meaning life and “metrics”
means to measure. (Ruud & Hornak, 2008). From the last few decades, automated biometric
systems have gained a lot of attention due to the advancement in image processing and
advancement in computer applications. Biometric technology is not old and has a long
history that goes back hundreds of years. Egyptian and Chinese are the major part in the
history of biometric. For the purpose of identification, the quantitative measurement of
human body dates back to the era of 1870s through the measurement system introduced by
the Alphonse Bertillon. (Komarinski, 2004) The system introduced by him was used in USA
until 1920s and to identify the prisoners the diameter of skull, length of arm and feet was
measured. 1880s, the identification through fingerprints and facial measures were used by
Henry Faulds and William Herschel. This system was automated in 1960s with invent to of
technological advancement and digital signal processing was introduced. Finger print
recognition was developed by Speaker and he was the pioneer in exploring the system. (Tico,
2001).
This system was used in high-risk access fields, financial transactions and personal locks.
1970 was the era of development and deployment of the system of hand geometry and it was
used by governments on large scales. Further development gave way to the technology of
automated personal identification and in 1980s; signature and retinal identification was
introduced followed by Iris recognition system. However, in today’s world, the focus is given
to iris, face, and retina and fingerprint recognition. Fingerprints recognition has the
significant value among all other techniques. (David et al, 2005)
The first systematic capture was recorded during 1858 and Sir William Herschel was the first
man who used hand and fingers for identification purpose. He used the recorded the hand
prints of the workers at the back of the contract to distinguish them in Civil Services of India.
(Komarinski, 2004)
Alphones Bertillon used the body measurements, physical description and photographs for
identifications during 1870. This method was known as Bertillon age and in 1903it was
aborted after discovering that some people share same measurement. (Suny, 2003). In 1892, a
classification system was developed by Francis Galton who used minutia characteristics of
fingerprints for identification and is still being used by researchers. (Vacca, 2007)
In 1896, Edward Henry used fingerprints to identify the prisoners using Glaton’s theory. He
also developed a system in which thousands of fingerprints were easily filed and traced.
(Office of Scottish Criminal Record, 2002)
Iris pattern for identification was used in 1936 by Frank Burch and Woodrow developed the
face recognition system that was semi-automatic. That system used the eyes, nose, ears and
mouth in a photograph for identification. (Woodward et.al, 2003)
In 1965, first signature identification system was developed by North American Aviation and
was later used by FBI in 1969 for cases analysis. At the end of 1994, advanced stage of
fingerprint automation occurred and Integrated Automated Fingerprints Identification System
was developed. The system was used to meet the three major challenges that were; digital
acquisition of fingerprints, ridge extraction, ridge pattern matching. (David et al, 2005) In
1993, AFIS (Automated Fingerprint Identification System) was developed first time by Palm
System. Defence Nuclear Agency and Iriscan was commercially introduced in 1995. After
2000, various advancements were made in identification methods and finger recognition was
considered the best and was accepted around the globe as successful identification measure.
(Wayman, 2005)
2.2 Biometric System Types:
The commonly used biometric techniques are as follows:
Deoxyribose Nucleic Acid (DNA):
DNA is also considered unique for every individual. However, the identical twins have the
same pattern of DNA. In forensic application it is most commonly used but Vacca (2007) is
of the view that DNA is not reliable and significant because of the privacy issues, real-time
recognition automation and contamination.
Fingerprints:
This is the most widely used and accepted method of biometric identification system. It is
considered more robust and reliable than all other methods because of the unique surface of
the fingers. (Wing, 2005) The users just have to touch the surface of the biometric sensor and
do not need to type the passwords or PIN code that are highly vulnerable to frauds. (Ruud,
2004)
Voice Recognition:
This method uses voice for identification system. The voice is identified using a short
utterance. The acoustic features of the voice are used to differentiate the individuals. The
disadvantage of this system is the background noise. In phone-based application this system
is most appropriate. (Matsumoto and Hoshino, 2002)
Ear:
According to Miller (1998) the cartilage and the structure of the pinna and the shape of ear is
unique feature of a human. The recognition process for ear is performed by matching the
various points of pinna with the central position in an ear. However, this method is not
appreciated much for personal identification as these features are not very distinctive.
Face:
Various techniques have been used to capture the image of face. Using camera for the face
recognition method extract the features of the face by capturing the image from central point.
Matsumoto and Hoshino (2002) describe that face recognition is beneficial because of its
characteristics of being hands free and non-intrusive. However, the image sizes are big and
require space between 100 bytes to 1 mbs. (Cantzler and Robert, 2001)
Iris:
According to Kova, Jure and Solina (2003) iris of every human is unique measurably. In a
video, that is fairly clear it is very easy to detect in an external environment. Pattern of ones’
iris contains randomness at a greater level. These patterns are different even for the identical
twins and are believed to remain stable throughout life. It is the second best identification
method after fingerprint. (Kung and Mak, 2004)
Retinal Screening:
The vascular patterns of the eye are used for the identification. The individuals who are
healthy, pattern remains unchanged during a life time. (Hull and Jackson, 2001)A low
intensity light is used to scan the retina. The use has to look into the device to input data. It is
essential to look in the eyepiece to get a clear image.
2.3 Fingerprint Recognition System: Past to Present
According to research conducted by International Biometric Group (2003), detection through
finger prints have been studied scientifically for a number of years. Fingerprints
characteristics were studied in early 1600s. However, use of fingerprints as identification of a
human was used in mid 1800s. Sir William Herschel was the first to research that finger
prints of a human being does not change over time and pattern of every human being’s finger
are different and unique. (International biometric group, 2003). Based on his findings the first
system to use fingers for identification was implemented in 1877.
Initially one-to-one identification process was used to use handprints for identification. With
further developments in the system Henry Classification system was developed by Edward
Henry in 1897. According to system all ten fingers were logically categorized on the basis of
pattern of fingerprints. Until mid-1990s this classification system was used in various
organizations across the world for storing images. (Jain, Ross, & Prabhakar, 2004).
2.4 Fingerprint Features and Detail:
In fingerprint recognition various features are involved and Galton developed level 2 of the
features and ridge ending and ridge bifurcation with respect to minutia points was the basic of
level 2 definitions. A probabilistic model was also developed by Galton that was used to
quantify the fingerprint uniqueness on the basis of minutia points. (Galton, 1965) according
to his research sweat pores can be discovered on ridges but no further study was made to use
pores for identification purpose.
The study of sweat pores for identification was done by Locard in 1912, and according to him
sweat pores may also be used for identification and they are also permanent and unique.
(Locard, 1912) he also presented the four categories that can be used for identification
purpose using the sweat pores. He stated that size, position, form and number of pores
frequency may be the basis of identification. (Wilder and Wentworth, 1932). Further study
discovered that to identify a person 25-45 pore are sufficient whereas, the number of pores on
a ridges ranges from 10-19 and 23-46 pores per inch. (Ashbaugh, 1999)
Endoscopy was also discovered by Chaterjee in 1962 as fingerprint identification method;
this refers to the use of combination of edge and friction of ridges to develop unique identity.
He categorized the shapes of ridges edge in to eight divisions namely, pocket, convex,
concave, angle, peak, straight, table and others. (Ashbaugh, 1999)
Further research discovered that friction ridges and all edges along ridges can be placed in
one of these divisions and edge shape changes because of difference in the growth patterns.
Coroscopy and endoscopy have been the focus of the researchers of ridgeology they believe
the sweat pores and shape of edges of the ridge are also unique and permanent and play an
important role in fingerprint recognition. (Demirkus and Jain, 2007)
2.5 Sensing Technology for Fingerprints:
Contingent upon whether the securing methodology is offline or online, a fingerprint may be
either (i) an inked fingerprint, (ii) a latent fingerprint or (iii) a live-scan fingerprint. Inked
fingerprint is used to indicate that the fingerprint image is obtained from an impression
Figure 1: The six major fingerprint classes: (a) arch, (b) tented arch, (c) left loop, (d) right loop, (e)whorl, and (f) twin-loop
Figure 2: Examination of distinctive unique fingerprint impressions: (a) an inked rolled fingerprint (from NIST 4
database); (b) an inked dab fingerprint (from NIST 4 database); (c) live-scan (dab) fingerprint (captured with a scanner
manufactured by Digital Bio
to obtain the pattern of ridges and valley of fingerprints various methods are used. In past,
clay and later in wax were used for fingerprint images. In 19 and 20 century ink technique
was used most commonly for taking fingerprints and was referred to as fingerprint sensing
offline. In forensic applications still the same method is used. (L, O’Gorman, 2003). Live
scan sensors were also developed but they were not successful as they did no gibe good
results when fingers were wet or dry. These sensors were known as FTIR (Frustrated Total
Internal Reflection). Last two decades observed remarkable development in technology of
fingerprint sensing. Multi spectral fingerprint imaging in one of them. This system used to
scan the fingers surface using light of different wavelengths. The quality of images was far
better in MSI. (Rowe et al, 2005)
The technology of multi-camera was also invented by TBS Inc., in 2006; this system was
called touch less images. The direct contact of the skin is avoided in this method and image is
accessed without deformation of the skin. (Parziala, 2006)
The latest invention is P3400 it is a high resolution device that is used for finger print
identification. It can produce more than 500 Dpi high quality images. MFS-500 is another
latest sensor that can also be applied in 3D rolling and printing scan of finger for identity
identification. (Zvetcobiometrics.com, 2009) The application and environment where a
scanner has to be implemented specifies its type. Generally, the scanners that have the
specifications of automated fingerprint identification system with high image quality, size,
accuracy, and signal to noise ratio, pixels and geometry and are certified from FBI are
preferred in US. (Fbibiospecs.org, 2008)
2.6 Fingerprints Detail:
According to definition of Jain, Ross and Prabhakar (2004) finger prints patterns are
comprised of the ridges and valleys sequences. Ridges appear in an image as a dark line
showing the pattern and light area between the dark lines or ridges reflect valley. A cut or
burn on the finger does not affect the pattern and structure and same pattern is reconstructed
when new skin is reproduced. Maltoni and Prabhakar (2003) explain that ridge lines have
distinctive patterns and unique shapes. This shape is characterized by curvature and is known
as singular region. This singular region is further characterized as delta, loop and whorl.
These three regions of singularity help in dividing the fingers into five major distinctive
categories.
Biometric systems are used to identify the unique patterns of fingerprints for various
purposes. Image acquisitions using fingerprints is easy and secure way for verification and
identification and are applicable not only for forensic investigation but also for civilian
identification. Finger print recognition can be divided into two main categories that are:
•
Fingerprint Identification
•
Fingerprint Verification
O, Gorman (2003) defines verification refers to the final step of the whole process of
recognition and verify the identity of a person. A unique identification number is given to the
person and users are requested to give fingerprints along with the assigned identification
number. Whereas, the identification of finger prints specifies a person without identification
of number. Simply, fingerprint is taken and then a whole database is searched for having the
similar image stored in it.
Verification vs. Identification
Verification
Identification
My user name is ABC-1. Does the
What is the name given to my
data stored for my user name
biometric data when it is
match my biometric data?
presented?
User
Biometric
ABC-
10101010
1
Table 1
2.7 Fingerprint Acquisition Method:
This section explains the methods that are used to acquire the fingerprints of an individual:
2.7.1 Optical Method:
Optical fingerprint is acquired through visible lights that capture a digital image and print
them. It is basically a specialized camera that works as a sensor. The finger is placed at the
touch surface, the top layer of the camera and the light-emitting phosphor layers that is
beneath the top surface send light to fingers and illuminate them. An array of solid state
pixels is developed when light passes through the finger and an image is captured. (Raul,
2007) However, according to the research of Saeed and Majid (2005) a bad image is
generated as a result of scratchy or dirty surface of camera. Another disadvantage is the dirty
figure that also results in the bad image. Furthermore, this is not capable of sensing a real
figure finger and a fake one.
2.7.2 Ultrasonic Method:
These make use of ultrasonic sensors while making use of the basic principles as used in the
medical ultra sonography process for the purpose of being able to create certain visual images
of the fingerprint. Different from optical imaging process, the ultrasonic sensors make use of
certain very high frequency sound waves for the purpose of being able to penetrate the
primary epidermal skin layer of the finger. Sound waves are generated by making use of
piezoelectric transducers along with reflected energy and these are carefully measured by
making use of piezoelectric materials. This becomes possible as the dermal layer of the skin
of the finger tends to exhibit similar characteristics of the fingerprint and the wave
measurements that are reflected gets used for the purpose of being able to create an image of
the same. In this process there remains no need for an undamaged or clean epidermal skin or
sensing surface. According to Jain et al (2008), capacitance sensors make use of certain
standards that are directly connected with capacitance for the purpose of being able to
structure the pictures of the chosen fingerprints. This is technique of imaging in which the
pixels are shown by the sensors. From these pixels one goes in the form of a single plate of
capacitors. The dermal layer goes in the form of another plate and the epidermal layer goes in
the form of dielectric plate. The capacitor is parallel plated, the dermal layer is electrically
conductive and the epidermal layer is non-conductive. (Lee, and Wang, 1998)
2.7.3 Capacitance Method:
Setlak, et al (2005) states that this method as powerful and accurate as ultrasonic method. It
uses the capacitance sensor to form the image of the finger and a sensor creates the array of
pixel on capacitance surface and resultantly, dermal layer is conductive electronically.
According to him the passive capacitance work on the same principle as other sensors and
uses dielectric constant of epidermis and dermal layer for imaging the patterns of ridges and
valleys of finger. He further described that sensing elements of dialect constant are known as
values and these values are used to differentiate valleys from ridges. Ye et al, (2007)
however, is of the view that active capacitance produces more accurate and quality results,
because, to measure the fingerprints ridges and valleys, a charging cycle is used that applies
voltage to skin and electric field is produced that illuminates the pattern of ridges and valleys
on sensor. He further states, that a discharge cycle is used to compares reference voltages of
the dermal layer and work same as ultrasonic method as it does not need to have clean
surface of skin or the sensor.
2.8 Fingerprint Enhancement Techniques:
Enhancement of fingerprint refers to the technique that is used to make the captured image
clearer and enhanced than the originally stored image to be used in further operations.
(Sherlock et al, 1994). It is a pre-processing of the acquired image that is use to improve the
fingerprint imaging. (Chikkerur, 2005) enhancement is used to improve the quality of low
and average quality images to clear the ridge structure and orientation. A common ridge
orientation is difficult to define in a noisy region and image is therefore enhanced for
extraction of feature and the whole process is referring to as pre-processing. (Gavindaraju,
2005) pre-processing of image includes; noise reduction and enhancement of contrast. A
contrast enhancement brightens the curves and creates contracts between dark and light
curves. (Paul and Lourde, 2006)
Hong et al (1998) method of finger print enhancement is the most cited technique. This
technique is based on the image convolution using the Gabor filters for ridge orientation and
frequency. Gabor filter (Gabor 1946) provides the maximum joint resolution with the help of
frequency –selective and orientation selective properties in domain and special field. So to
obtain the original structure of ridge and valley and to remove the noise Gabor filter is highly
beneficial. (Hong et al, 1996) However, Hong’s work was modified by Yang et al, (2003) and
he removed the sinusoidal plane wave assumption considering it incorrect and by making the
fingerprint process independent by introducing parameter selection process
Figure 3: An even-symmetric Gabor filter: (a) Gabor filter tuned to 60 cycles/width and 00 orientation; (b) corresponding
modulation transfer function (MTF)
Figure 4: Fingerprint Enhancement Algorithm. © Academic Press
Figure 5: Examples of enhancement results; (a) and (c) are the input images; (b) and (d) show enhanced recoverable
regions superimposed on the corresponding input images.
Figure 6: Fingerprint Enhancement Results: (a) a poor quality fingerprint; (b) minutia extracted without image
enhancement; and (c) minutiae extracted after image enhancement
to more dependable highlights. A single block direction can never genuinely represent the
direction of all the ridges in the block and may subsequently present filters antiques. One
normal directional filter utilized for fingerprint enhancement is a Gabor filter. Gabor filter
(see Fig. 2) have both frequency-selective and orientation-selective properties. Case in point,
an appropriately tuned Gabor filter will pass only fingerprint ridges of certain spatial
frequency streaming in certain specific direction.
Fourier domain method proposed by Sherlock et al, (1994) was another fingerprint
enhancement technique. This method used pre-computed filters that were convolved with the
image of finger and presented various filtered images. The image having the closet
orientation with the original image was selected. Another method for image enhancement
was developed by Teddy and Martin (2002) using the technique of spectral analysis. Latent
fingerprint were found blur, unclear and incomplete after spatial analysis. He then used
frequency analysis to enhance such images with the help of high pass butter-worth and bandworth filters. However, this method needs to use both techniques that are spatial analysis and
frequency analysis to enhance image and was not appreciated much. (Chikerrur, 2005)
Short Time Fourier Transformation Method (STFT) was proposed by Chikerrur (2005) that
was based on algorithm. In this method, instead of using Fourier system response Chikerrur
used frequency and dominant ridge probabilistic approximation for image enhancement. By
performing the STFT analysis, images of ridge orientation, ridge frequency and foreground
region were created.
An adaptive filter technique was introduced by EKyung (2006) and instead of filtering
images uniformly, they proposed the use of oily/ dry and neutral images. They argued that
five features that are: Variance, Mean, Block Directional, orientation changes rate and
Thickness ration of Ridge and valley may be used in Ward’ clustering algorithm to enhance
the image. This process was criticized by various researchers as after clustering, different
processes had to be applied on oily and dry images. (Chengpu, 2008) Such as, if image is dry,
the central lines of the images were enhanced by removing white pixels for ridge extraction
and for oily image, thin and disconnected lines were dilated to enhance valley. In case of a
neutral image, no further process was required.
Chengpu et al (2008) the proposed another method for enhancement of fingerprint images.
He used Diffusion filter method and Gabor filter as a combination and developed a low pass
filter and band pass filter to get the high quality image.
2.9 Fingerprint Matching Technique:
The technique for fingerprint matching refers to the comparison of the fingerprints images to
find similarity. The fingerprints images may sometimes not clear due to noise, feature
extraction nature such as spurious and missing minutiae. (Fernendez, 2009) Therefore, it is
essential to have an algorithm for matching that is immune to such errors and provide
accurate results. A similarity value (id) the output of matching algorithm which ensures that
decision taken on the basis of comparison is accurate. (Fernendez, 2009).
Given two (test and format) representations, the matching module figures out if the prints are
impressions of the same finger. The matching stage regularly characterizes a metric of the
closeness between two fingerprints representations. The matching stage likewise
characterizes an edge to choose whether a given pair of representations fit in with the same
finger (mated pair) or not. A commonplace strategy for unique finger impression
coordinating is to first adjust the unique finger impression representations and at that point
look at the prints for comparing structures in the adjusted representations. Since solutions to
both the issues (alignment and correspondence) are between related, they are (certainly)
solved at the same time.
Figure 7: Two separate impressions of the same finger. In order to know the correspondence between the minutiae of
these two fingerprint pictures, all the minutiae must be absolutely restricted and the disfigurements must be
recuperated.
Figure 8: Difficulty in fingerprint matching. (a) & (b) have the same global configuration but are images of two different
fingers.
2.9.1 Minutiae Based:
This method uses finger images to extract minutiae. This extracted minutia is then compared
to the templates previously stored in the database (Cantzler and Robert, 2001). Most of the
time, the two dimensional plane is used to store the minutiae details as a set of points. And to
indicate the location of the data recorded the X- and Y coordinators are used. Other
parameters are also used and may base on the type and angle of orientation of the minutiae.
For accuracy of the output, significant amount of processing is involved in this method. (Jain,
& Prabhakar, 2003) variety of methods has been used by the biometric systems to extract
minutiae such as thinning the image of fingerprints and performing a scan across all image of
the finger using a pixel by three pixel block. The simplest way of finding the correspondences
is to consider a pair of minutiae as matched minutiae if the distance between them and their
directional difference are smaller than some pre-specified thresholds (e.g., 15 pixels in
translation and 20◦ in rotation).
Figure 9: Example of matched minutiae from a fingerprint pair (latent and rolled print).
2.9.2 Image Based:
Another method is image-based technique. According to (Seow and Abu, (2002) this process
is quite simple and pre-processing does not requires significant processing amount. These
techniques consist on banalization and that is the only pre-processing requirement of this
method. The accuracy and computation of this method is better than minutiae extraction and
it produces far better results even in low quality images.
Image based processing also provides help in rotation of image. When the input image is
different than the image found in template rotational correction can be applied to check the
accuracy. Various biometric systems use the technique of correlating the template and the
rotational pixels to produce the best outcomes. Highest the correlation value of the image
significant is the alignment between template and the input image. (Wing et al, n.a)
Ito et al. (2005) has found the similar technique in his research using the component phase to
determine the similarity between the two images that is an input image and the template.
When threshold of the system is lower and the matching score exceeds the image is treated as
similar.
Wavelets are also used in the imaged-based technique of fingerprint matching. Wavelet
domain features are used by this technique to match the patterns of fingerprints. The benefit
of this image is the extraction of the image without involvement of the pre-processing. A
rectangular image is developed around the core when the core patter is established.
According to Hull (2009) Core pattern refers to the central sub image. This sub image is
further divided into the uniform size blocks and decomposition of the image occurs with
computation of wavelets. (Kuosmanen, & Saarinen, 2001).
2.9.3 Ridge Feature Base:
Minutiae extraction becomes difficult in many cases and quality of the output image also gets
affected. Minutiae extraction algorithm is impacted by the pre-processing, low quality of
image and noise. Ridge features provide variety and quality in the output. The shape, size and
silhouette of the finger can also be computed and analysed for identification along with the
number, size and location of the singular regions. (Miller, 1998)
Levin et al., (2002) states this method therefore uses the geometrical attributes and spatial
relationship of the ridges lines for example the sweat pores are also unique for every
individual however, an advanced system is required to use them for identification and
detection. Ridges features are best for low quality images and provide equally good output as
a good quality image does. It’s another benefit is that it can be used in conjunction with good
quality and minutiae based images. (Levin et al, 2002)
JAIN et al.: FILTERBANK-BASED FINGERPRINT MATCHING
Figure 10: Ridges and automatically detected minutiae points in a fingerprint image. The core is marked with a ×.
2.9.4 Correlation based matching
Habib (2013) defines a correlation based technique is used for matching and comparing two
sets of fingers. The coefficient of correlation of every finger is computed by aligning the
fingers properly according to this technique. However, in view point of Ashbourn (2004) in
case of need of rotation, this method does not show the exact results. Also, it is difficult to
compute and calculate in case of large data base system. This method is also prone to error
when it comes to the noisy regions and distortion in the data. The relationship between two
fingerprint pictures is registered for alignment, which can be performed all globally or
locally. The pictures of different impressions of the same finger may seem altogether
different because of pressure variations (ridge thickness, contrast, worldwide structure, and
so on), which to a great extent influences the connection between two pictures. Additionally,
the systems included in this classification may be computationally exceptionally expensive.
2.9.5 Hybrid Methods
Hybrid method is developed by the Habib and Hameed (2013) by combining the two research
methodologies. Wayman (2008) has researched that to match the fingerprints; minutia and
edge stream methods may be combined to get a single method. Jain et al (2008) is however of
the view that combining the minutia method any other method affects the significance of its
performance.
The study conducted by Braghin and Chiara (2002) suggest, that correlation based may be
combined with any other method for matching purpose and minutia based matcher is the best
to develop a hybrid with.
The extraction of the surface through minutia gives more
significant results comparing to other methods.
2.10 Biometric Characteristics:
The development of large scale computer networks and related applications to use such
networks has increased the risk of the theft and identity related problems for the users. The
design of identification system has made the use of computer networks and identity
verification simple and easy. A biometric system offers accuracy, rapidness, reliability and
security in personal identification of the users. (Wayman, 2000) A biometric system that
offers robustness, distinctiveness, acceptability, availability and accessibility are considered
the best and of high quality. Ashbourn (2004) stated robust refers to the quality of remaining
unchanged over a time on an individual, distinctiveness refers to the variation over the whole
population, and acceptability refers to the quality that people accept the measurement to be
taken from them and do not object whereas, availability means that this measure is ideally in
multiples on entire population. For these five qualities the quantitative measures have been
developed and false non-match rate is used to measure the robustness. It is the probability
that enrolment image will not be matched by any submitted sample. False match rate that is
also known as Type II error is used to measure distinctiveness. It is the probability that
submitted image will match the image of other enrolling user. (Farina, Vajna, leone, 1999)
Failure to enrol rate is used to measure the failure to enrol rate this means failure of the user
to submit a readable image for enrolments in a system. Throughput rate is used to quantify
the accessibility. It measures that how many individuals can be processed in specific time
duration. Polling of the users is measured for acceptability. All four qualities that are
mentioned earlier are inversely related to the measures. (Wayman, 2000) For instance, lower
level of robustness means a higher false non-match rate. However, all the qualities are
dependent on other factors like application’s specifications, the physiological and
psychological state of the population and use of hardware and software systems.
2.11 Biometric Application:
The biometric application is achieving the operational goals that vary in nature according to
the needs of the users such as to search for the known and unknown individuals, for
verification of the claimed identity, to search the individuals having no identity or unclaimed
identity. The biometric system searches the templates from a database having millions of
previously submitted samples given at the time of enrolments of data (Ye et al, 2007)
Different applications are used for the verification of data. Some systems have the ability to
search multiple samples against a database that has a few models stored. Whereas, some
systems verify the samples against claimed and imposter identity both in a model.
(Kuosmanen, & Saarinen, 2001).
All verification and search is based on the application environment that varies with respect to
the use of device by trained or untrained people and supervised and unsupervised systems.
2.12 Biometric Application Types:
The uses of biometric can be categorized as follows:

Verification:
Biometric systems are used for verification purpose. Verification refers to the process to
ensure that a person who claims to Mr. X is really Mr. X or not? Verification is done by using
the centralized or distributed storage of the system. (Farina, Vajna, leone, 1999)
Centralized storage system stores all the data and associated entries in a single location of a
biometric system and it is easy to retrieve a claimed identity. The data of individual is
verified to ensure whether it matches or not. The example of verification by distributed
storage is the use smart cards or a memory device by an individual to prove his identity. (Lee,
and Wang, 1998)

Identification:
Rosenzwieg (2004) defines; identification is the process of discovering the unknown identity
of an individual. Central database is essential for identification process of an individual and
without a record or database identification is not possible. To identify Mr. X, his fingerprints
are taken and scanned. Then both the images are verified with the templates exists in the
previously recorded database. (Lee, and Wang, 1998)

Screening:
Screening is the process of maintain a watch-list. Watch-list may include the information
about individuals to be added or excluded from the database (Wayman, 2005) Biometric data
must be provided by the individuals to match the data. Scanning crowd through scanning
cameras to search a specific individual is an example. (Farina, Vajna, leone, 1999)
2.12.1 Biometric System Model:
Biometric authentication system is based on the five subsystems that are as follows:
Collection of Data:
According to Vacca, (2007) Biometric system uses the physiological or behavioural
characteristics for measurement. The key assumption used by a biometric system is that it is
distinctive and repeatable for the same individual over time. A sensor is used as a user’s
characteristic and uses behavioural component for image storing. (Braghin and Chiara, 2001)
The behavioural component of a biometric system varies with respect to the users and the
operational environment. Output of the data is dependent on the input and is convolution of
biometric system of the measures, presentation of the measures and sensor’s characteristics
that are technical in nature. (Ashbourn, 2004)
Transmission:
Some of the biometric systems take input from one location and store it on another. In such
systems data transmission is used. (Wing, 2005) Compression is done in case of larger
amount of data entries. Compression and transmission of the data is done before the image is
transmitted and data is expanded for further use. (Perkin, 2001) The standardization of the
transmission and compression protocol helps in building and reconstructing the original data.
The most used standards are compression of the fingerprints. (Levin et al, 2002)
Processing of Signals:
Signal processing is used for matching the user’s characteristics for other measures. Signal
processing is divided into four sub systems that are: segmentation, extraction (feature),
quality control, and matching of patterns (Kung and Mak, 2004). Segmentation involves the
search of transmitted signals and biometric patterns. For examples, the finger recognition
system first finds the boundaries of the finger in the image that needs to be transmitted.
(Kuosmanen, & Saarinen, 2001) Extraction involves the non-reversible compression and
original image cannot be extracted and transmission is occurred after feature extraction.
Matching of pattern involves the comparison of present pattern to the stored image and
sending a quantitative measure to the system.in case of multiple enrolments in a system,
pattern matching is skipped by the system and in a system where a claimed identity has to be
verified pattern matching compare on one template in the database. In case of larger amount
of data, pattern matching uses various models and templates for large scale identification.
(Kenovo, 2004)
Storage:
Depending on the biometric system, there are various forms of storage. After the pattern
matching process, the templates and models are stored by the enrolled users. Data is needed
to be stored for raw biometric patterns and raw data storage on the system allows the users to
store data from all enrolled users. (Kumar, 2009)
Decisions:
The subsystem generates the pattern on the basis of matches and non matches. The identity
claim could be rejected or accepted based on the decision policy of the system. In case of
specific search failure, the systems automatically search for the large pattern and unclaimed
identities (Braghin and Chiara (2002). In fingerprinting verification system, the user presents
his finger to the sensor and output is also figures that passes through the feature extraction of
the system and arrive at the template and it is then saved in the database of the biometric
system. The database might be law enforcement, central or smart cards or ID cards related
issue (Levin et al, 2002). For the purpose of verification, the finger print of the user or
individual is captured and is matched with the templates stored in the database. If both
images match and are closed, then the output decision comes as “yes”. The decisions may be
based on similarity and dissimilarity and otherwise the decisions come out as “No”. (Maltoni
& Prabhakar, 2003).
False Accept Rate and False Reject Rate are used to as the metrics to quantify the
convenience and accuracy for authentication. FAR refers to the probability that user access
will be accepted and FRR refers to failed attempt by the user or imposter. In case, when
FRR=FAR EER (Equal Error Rate is used. All matrices are dependent on the decision
template. (Cantzler and Robert, 2001)
2.13 System Design Issues with Biometric System
However, the biometric systems are also vulnerable to security issues and fake biometric
submission has been a security issue. According to the research conducted by Putte and
Keuning, (2003) there were several fake artificially created and accepted finger impressions
and images than original. They also presented methods for applying fake images with and
without cooperation. The qualities of images produced through fake fingers were much better
than the original.
Six sensors out of seven accepted the fake finger for the first time as original finger.
However, the seventh sensor accepted the fake finger at second attempt. The reason they
presented was that sensors are not well developed and lack the quality to distinguish a fake
and original finger based on the physical qualities such as heartbeat, conductivity etc.
According to Matsumoto et al. findings the probability of accepting the fake finger with
original was 68% -100%.
However, to deal with this issue of the biometric systems, Derakshani et al, (2008) has
presented two solutions based on the software applications. The software uses 5 seconds
video of the finger and has a capacitive sensor that can detect the heart beat and heat of the
body.
CHAPTER-3: RESEARCH METHODOLOGY
3.1 Introduction
The objective of this chapter is to present the methodology used in collecting information to
support the literature and objectives of the research. The emphasis is given to give a detailed
overview of the design and types of the data collected and the approaches used to collect
data. Data instruments validity and reliability will also be discussed.
3.2 Research Design:
In this paper, the explanatory research methodology has been adopted as the major research
design approach.
Explanatory research is conducted to acquire new insight in the
phenomenon and uses theories and models to explain hypothesis and phenomenon.
3.3 Explanatory Research Design
The explanatory research approach was also used to address the research objectives.
Explanatory research was conducted to study the use of biometric systems for fingerprint
identification system. The data will be collected from secondary sources to support the
literature and to have a deep insight in the topic. Secondary data will be obtained from
literature, online and offline libraries, books, journal and articles. Secondary data may be
prone to errors that are why only the recognized data source will be used.
3.4 Validity and Reliability:
Validity and reliability of research instrument is essential for effectiveness of the research
study. All data was collected through the authentic sources like journals, books and online
library and proper referencing was used to make research reliable. The information that did
not contain any reference was eliminated to ensure the validity.
3.5 Specification of the Research Model:
Various biometric techniques are used to provide the security using different models and
methods. Ensuring the secure images and the quality imaging is essential for biometric
system. Different security models have so far been developed by researchers such as
steganography, encryption and watermarking. For this specific research purpose the
cryptography is used for the image and security enhancement of the biometric system.
According to Kova¸ Jure, Peter and Solina, (2003) Steganography is the Greek word and it
means communicating secretly and hiding out the critical information. Steganography is used
to transfer the critical information and hiding out the information itself, such as transfer of
minutia data from client to main server (Munir and Javed, 2005). Cryptography focuses on
making the information encrypted that become meaningless to the other parties who are not
authorized to know. This technique is highly secured and eliminates the chances of
interception by outsiders. Watermarking however is used to protect the intellectual property
rights of the data such as company logo. (Ratha and Boll, 2004)
3.6 Biometric Cryptosystem:
Braghin and Chiara (2001) are of the view that biometric cryptosystem are highly reliable
and theoretically proven systems for the security of data acquired through fingerprint
identification. The data is built theoretically and is complex with respect to the structure and
proven against the infeasibility of attack.
Perkin (2001) suggests that among other encrypted methods, the most authenticated method
was considered the password. This method uses the key like strings to protect the data.
However, passwords were also prone to be hacked and copied by the others and data
encryption made it possible to protect the data using biometric matching and biometric based
key release. For fingerprint acquisition, biometric data acquisition follows the same rule. The
first step is collecting the data from the individuals and storing the data into the large
database systems (Jain et al, 2005) the digital sensor was used to record the data using the
640*480 pixels and using the colour intensity of 24 bits. For data collection, the sensor was
located at the wall and black cloth was used as the background colour. Finger placement on
the sensor was done in a way that a little gap was maintained. The data was collected from a
sample of 100 people of both genders from the age group of 18 and 25. Almost 95
individuals participated in the data collection.
3.7 Pre-processing:
According to the Minolta (2004) Pre-processing was performed on the data prior to the
experiment and four processes that included: the sampling normalization, finger extraction,
hand segmentation and feature template generation were applied on the data. The preprocessing was done in MATLAB.
3.8 Sampling Normalization:
Sampling normalization was used because of the variation in the placement of sensor and
also the pixels position was varied because of the adjacent image. Pixels moved between x
and y axis and. X axis was used to indicate the pixels and y axis represented the size of
interval in millimetres. The graphical presented showed that value of the collected data tend
to cluster at 0.43 mm. the images range at 0.4mm grid a d the distance of linear interpolation
was normalized.
Figure 11: Sample size in X direction and Sample Size in Y direction
3.9 Segmentation of Hands:
The researcher used the pixels that were in the range of an image and were lying at the
fingers surface. An image was taken of the finger intensity and correspondence was done in
pixel to pixel intensity and range of image. They further employed the method of combining
the edges and the skin of the finger so that a reliable finger image with strong intensity may
be captured to be segmented in the image range. The rule of colour skin RGB detection was
used in combination with module of Sobel edge detector. They used the following pseud code
in the module:
Algorithm for finger Detection using Biometric Identification
Input: I- (a x b RGB image of finger)
Output: C (a x b image of the binary edge of finger after detection from Canny edge detector)
Output: O (a x b fingers binary image)
Module begin with
C= Edge_Detecter_Canny
For I= 10 to a do
For j=10 to b do
If I (I, j). R > 90 AND I (I, j) G>45 AND I(I,j) B>25 AND max {I (I,j). R, I (I, j). B}-min{I( I,j).R,
I(I, j).G, I(I, j). B} >20 AND j I(i; j):R ¡ I(i; j):G, j > 15 AND I(i; j):R > I(i; j):G AND
I (i; j):R > I(i; j):B
Then O (i; j) = 1; (It is a valid mask pixel)
Else O (i; j) = 0; (It is a background pixel)
end if
end for
end for O = O [ C (union image of O and C is final output)
end of module
3.10 Finger Extraction:
To locate the valleys on the finger the convex hull of the hand profile id used. The ring,
middle and index finger are identified by using the symbols α, β and ᵞ. All these fingers are
placed individual to be extracted for identification of the valley position. Another code was
used to extract the finger to locate the valley positioning on finger: the code is:
Valleys Location Algorithm (c.v. list)
Input: c. list (coordination of x and y in an order that are lying on hand's outline)
Output: v. list (coordination of x and y in an order which are valley locations)
Begin module
c.nlist = Comm
Convex Hull contour (c. vlist)
n = Number of points in c. list
for i = 1 to n ¡ 1 do
pt = The interval poits [c.list(c.n.list(i)); c.list(c.nlist (i + 1))]
when the x coordinator is smallest
if pt 7= c.list(c.nlist(i)) AND pt 7= c.list(c.nlist(i + 1))
then
add pt to v.list
if
for
end module.
According to this algorithm presented, once all three fingers were extracted they segmented
the boundaries to connect the pixels in a hand contour. A binary finger mask was developed
after filing the closed curve. The corresponding pixels location was indicated by finger mask
in the range image and that were considered the valid pixels. The noise pixels were removed
from the image by removing the two pixels from the finger mask perimeter. They have
identified the pose variation of the fingers and rotated the corresponding pixels on finger
mask, consequently, the finger mask was rotated to the major axis and output range image
corresponded on the horizontal or x-axis. The finger mask was position five pixels on right in
the image that was sent as an output after following rotation process and placing the
corresponding range data in 80 x 240 finger image. The feature templates are generated
through output fingers and are used for further comparisons.
3.11 Feature template Generation:
The researcher further used the output image for computing the range data for
correspondence through curvature surface of the finger mask. For this process the linear
regression technique was used (Flynn and Jain, 2003). The surface point of the finger was
denoted by p and set of points were presented by Sp. They estimated the orthogonal vectors
on a normal surface that were cantered at P.
Figure 12: Valley Location on a finger
Figure 13: Pixel attraction on a finger
Figure 14: Finger Mask
The following equation was used by Vacca (2007) to estimate the vector points:
z = f (x; y) = ax3 + bx2y + zxy2 + dy3 + ex2 + qxy + gy2 + hx + iy + j
Then linear regression was used to locate Sp points. After that, they used the partial
derivatives to obtain the value of principle curvature. Lmin and Lmax. These values were
calculated using the following formula:
This estimation was done to calculate the noise contain by curvature. To limit the noise of the
data range, they arranged and smoothed the data prior to estimation process. Through this
approach a large data range was smoothed to obtain the fine surface of the finger. Vacca
(2007) also a large number of data points were used to address the problem. They used
different windows for varying points from 4x4 and 16x16pixel correspondence in a 2D
format. The window size that became optimal was 81 pixel points that corresponded to
3.5x3.5 mm extent of the 2D.
The feature templates are employed after computing the principle curvature using curvature
based surface. H and K classification was used by the Jain (2004) that is a very widely and
most commonly used method of the classification and most appropriate in this research. This
method is used to determine the shape of the surface at a particular point and H and K signs
are used to depict the values.
However, the problem he encountered in this method occurred due to the noise that affected
the data precision and errors such as cancellation, rounding of figures and truncation. This
resulted in the minimum chances of getting the H and K equal to exact zero. To address the
issue, they chose a threshold Z zero that was used to make the value equal to zero. The
threshold was:
If (H;K) <Z zero
Then
H or and K value will be equal to zero at that point.
According to HK method, the finger classification was as Z Zero= 0.001 and Z Zero= 0.002.
The surface type according to the H and K method is shown in the figures below:
Figure 15: Z=0.001
Figure 16: Z=0.002
As the position of the biometric sensor was varying during the research a figure using the
threshold method to get a value equal to zero was difficult to extreme. To solve this issue, the
version presented by Dorai and Jain's (2004) was used for classification of shape index.
H and K Colour Map Classification
Type of Surface
Colour
Type of Surface
Colour
Valley
Yellow
Pit
Purple
Saddle Ridge
Greyish
Saddle Valley
Dark Blue
Peak
Green
Minimal
Rust
Flat
Black
Ridge
Red
Table 2
The need of Zero threshold was eliminated when they applied the shape index on the values.
Shape index was computed by SX (i, j) and each finger image was used as the feature
template. The image having the shape index of 80x 240 image pixels was generated using
this formula. The lower shape index values were indicated by dark areas in intensity image.
3.12 Matching Techniques:
The matching techniques that were applied on the data by Braghin and Chiara (2003) were
based upon the classification of the surface shape. The index images were compared using an
equation that was:
They argued that Cp (I,j) and Cg (I,j) are the gallery images and the pixel mask is presented
by N. the I denotes the indicator and if the argument is zero it returns unity. According to
him, matching score of the fingers is the shape class pixels’ correspondence and are valid in
images and classification of the images.
Another technique was adopted by Cantzler, Helmut and Fisher (2001) who employed value
of the actual shape index instead of assigned class value to shape. He used the correlation
coefficient for matching score:
The proper alignment of the finger was done for the accurate matching of the fingers. In each
output image the finger mask was aligned automatically to the centre during pre-processing
procedure. The overlapping pixels were computed using the three offsets that were vertical
(+,-1 pixel, no offset in pixels) offsetting was done to maximize the pixels during matching
when there was an overlap. Almost 20,000 pixels were included in an overlapping set. It was
analysed that rotation of the image did not bring any changes in performance of matching.
Jain and Dorani (2004) not only examined the biometric performance of each finger but they
treated all three fingers from a separate system. They used the fusion method described by
Hong et al. (2005) afterwards; the rule of Kittler et al, (2006) was also implied to get the
multiple values.
The equation presented by Kittler method was:
The match score values are presented by α, β, and ᵞ. The N in the equation denotes the total
number of the matches done in a given time period for identification and verification.
Whereas, n indicates the number of sample in one attempt. The median rule was also applied
as fusion method and the formula used was:
According to this equation the α, β, and ᵞ are each finger’s match scorer. Whereas, median is
calculated for all the matched score during the attempt of verification and identification.
(Kung and Mak, 2004)
3.13 Experiments for Verification:
An open universe model is used by verification. This model was described by Phillip et al,
(2009). According to this model, in the gallery set of images a probe set of subject image may
or not be presented. To conduct a verification test, a gallery set of 140 and probe set of 180
subject images was taken. In total, 360 unique subjects were used for matching. Every
individual participant or subjects’ eight finger sample was taken and total number of images
was 1,440. For this matching technique the configuration was used and that resulted in the
verification of 300 verification experiments. For verification purpose the comparison was
done among the same fingers. The code used for verification was:
Algorithm of Verification for Matching Purpose
Verification (K;L)
Output: FAR (False acceptance rate for 100 threshold values)
Output: FRR (False rejection rate for 100 threshold value)
Module begin with
FAR = [0]; (List entries set to zero)
FRR = [0]; (List entries set to zero)
For each UK 2 K (Probe Set Images)
For each UL 2 L (Gallery Set Images)
CMS = UK and UL Computed Match Score
JOC = bCMS=0:01c; (Applicable Threshold Value Search)
if (UK :ID = UL:ID)
if JOC ¸ 1
Increase FRR (JOC + 1) to FRR(END) by one;
else
Increase FRR(1) to FRR(END) by one;
end if
end if
if (UK 6= UL)
if JOC ¸ 1
Increase FAR(1) to FAR(KOC + 1) by one;
else
Increase FAR(1) by one;
end if
end for
end module
CHAPTER 4- RESULTS AND DISCUSSION
4.1 Results and Discussion
This chapter applies the result of matching process into a 3D matcher. Jain et al (2004) used a
database containing thousands of images. The experiment was done on the images collected
on the same day and previously collected images on the samples. Identification experiment
was performed on 60% of the selected population and verification was done of the 40%
population selected for the research. Following are the findings of the matching techniques:
The performance of the fusion rule on each finger was significant and as the time span
between the gallery and probe sets increased the difference in the performance of the rule was
more explicit and apparent. The average fusion rule was best in case of the identification
process. All of the fingers almost display the same results and no finger could be separated
for showing the better results. In case of all fingers the average results were out in all
configuration methods such as single, multiple, gallery and probe.
When a single finger was used for experiment that showed better performance after applying
the correlation coefficient technique. Furthermore, the surface of the finger showed the
performance as good as face recognition using the 2D method. When the identification
experiment was configured on finger and face using the biometric system, there was
difference of 1% in the results. This signifies the performance of the system and a new way
open to explore new technology.
The result also suggested the correlation coefficient is better matching technique in biometric
system than the shape class index and that is because of the way the matching scores were
calculated. Also, the matching score of a shape class is counted as a pixel and same surface
classification is used for shape index value for gallery and probe image sets. It may also be
extracted that corresponding pixels between the shape indexes belong to different surface
classification despite having the close values. This may lead to incorrect classification that is
why using the correlation coefficient is the best technique to avoid such issue.
Another error may occur due to the inter-session variations in the data caused by the incorrect
classification. This issue appeared when large number of data was used for identification and
verification experiments. However, by applying gallery and multiple probe images, the equal
error rate came out as low as 5.5% with 94% accuracy in identification. This shows that
fingerprint does contain the uniqueness and can be used for developing unique identity for
security.
CHAPTER FIVE
SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.1
Summary of the Study
The study examines the Contributions to Biometric Identification: Fingerprint for Identity
Verification. Specifically, the purpose of the study is to evaluate the verification and
identification of biometric fingerprints, determine whether there is biometric accuracy of
fingerprints of people over time, assess the reliability of biometric fingerprints in forensics,
examine the security level of biometric fingerprint compared to other biometric methods;
among others. Literature was reviewed which include the concept of biometrics, applications
of biometric systems, fingerprint recognition representation and approaches, biometrics
authentication techniques among others. Hypotheses were formulated through the usage of
the research questions.
5.2
Summary of Findings
Major findings of the study reveal that there is a significant relationship between verification
and identification of biometric fingerprints. The study also reveals that a significant
relationship exist biometric accuracy of fingerprints of people and the consistency of its use.
There is also a significant relationship between the long-term stability of biometric
fingerprint and its effectiveness in the changing world. There is a relationship between
universal acceptance of biometric fingerprint and social crimes Based on the findings of the
study, recommendations were made.
5.3
Recommendations
Based on the above conclusion, the following recommendations are made:
Biometric systems require an intimate association between people and the technologies that
collect and record their biological and behavioural characteristics. This is true whether the
application is overt or covert, negative claim or positive claim. It is therefore incumbent on
those who conceive, design, and deploy biometric systems to consider the cultural and social
contexts of these systems. Unfortunately, there are few rigorous studies of these contexts.
Below is a framework for developing a portfolio of future research investigations that could
help biometric systems better cope and perform within their cultural and social contexts.
Cultural and social issues arise at essentially two different levels, for the individual and for
society. At the level of the individual, whether they are interacting actively or passively with
a biometric system.
5.4
Suggestions for Further Research
The limitations of this study calls for possible recommendations for further research. The
researcher suggests that:
Further research should investigate that social considerations are critical in the design,
deployment and functioning of biometric systems. As we have noted, system performance
may well be degraded if relevant social factors are not adequately taken into consideration.
For example, religious beliefs that call for adherents to cover their faces in public make
facial-recognition biometrics problematic. Thus if a biometric system is to work well for a
broad range of people it must take into account behaviours resulting from such things as
religion or social convention. Every biometric system has a protocol for how it is to be
interacted with. The protocol may be simple or complex, uniform in application, or tailored to
the individual. Obviously, however, a good protocol for a biometrics system must recognize
variations in biological features. A system based on fingerprints must have ways to gracefully
accommodate a person who is missing a finger or who otherwise does not have usable
fingerprints.
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