International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE) ISSN: 0976-1353 Volume 14 Issue 2 –APRIL 2015
902
FINGERPRINT BASED ATTENDANCE SYSTEM
PROF. MEGHA.A.PATIL
Assistant Professor Department of Electronics and Communication Engineering
BLDEA’s V P.Dr P.G.Halakatti college of Engineering and Technology
Vijayapur-586101
Karnataka, India [email protected]
Abstract— Managing staff attendance during working days has
become a difficult challenge. The ability to compute the
attendance percentage becomes a major task as manual
computation produces errors, and also wastes a lot of time. For
the stated reason, an efficient attendance management system
using biometrics is designed. This system takes attendance
electronically with the help of a finger print device and the
records of the attendance are stored in a database. Attendance is
marked after staff identification.For student identification, a
biometric (fingerprint) identification based system is used. This
process however, eliminates the need for stationary materials and
personnel for the keeping of records. Eighty candidates were
used to test the system and success rate of 94% was recorded.
The manual attendance system average execution time for eighty
students was 17.83 seconds while it was 3.79 seconds for the
automatic attendance management system using biometrics. The
results showed improved performance over manual attendance
management system. Attendance is marked after staff
identification.
(Keywords: fingerprints, attendance, enrollment, authentication,
identification)
I INTRODUCTION
In an increasingly digital world, reliable personal authentication has become an important human computer
interface activity. National security, e-commerce, and access
to computer networks are some examples where establishing a
person‟s identity is vital. Existing security measures rely on
knowledge-based approaches like passwords or token-based
approaches such as swipe cards and passports to control access
to physical and virtual spaces. Though ubiquitous, such
methods are not very secure. Tokens such as badges and
access cards may be shared or stolen. Passwords and PIN
numbers may be stolen electronically. Furthermore, they
cannot differentiate between authorized user and a person having access to the tokens or knowledge. Biometrics such as
fingerprint, face and voice print offers means of reliable
personal authentication that can address these problems and is
gaining citizen and government acceptance.
1.1 Biometrics
Biometrics is the science of verifying the identity of an
individual through physiological measurements or behavioral
traits[1][2]. Since biometric identifiers are associated
permanently with the user they are more reliable than token or
knowledge based authentication methods. Biometrics offers
several advantages over traditional security measures. These
include a. Non-repudiation:[1] With token and password based
approaches, the perpetrator can always deny committing the
crime pleading that his/her password or ID was stolen or
compromised even when confronted with an electronic audit
trail. There is no way in which his claim can be verified
effectively. This is known as the problem of deniability or of
‟repudiation‟. However, biometrics is indefinitely associated
with a user and hence it cannot be lent or stolen making such
repudiation infeasible.
b. Accuracy and Security: [1]Password based systems are
prone to dictionary and brute force attacks. Furthermore, such systems are as vulnerable as their weakest password. On the
other hand, biometric authentication requires the physical
presence of the user and therefore cannot be circumvented
through a dictionary or brute force style attack. Biometrics has
also been shown to possess a higher bit strength compared to
password based systems and is therefore inherently secure.
c. Screening: In screening applications[2], we are interested in
preventing the users from assuming multiple identities (e.g. a
terrorist using multiple passports to enter a foreign 3 country).
This requires that we ensure a person has not already enrolled
under another assumed identity before adding his new record
into the database. Such screening is not possible using traditional authentication mechanisms and biometrics provides
the only available solution. The various biometric modalities
can be broadly categorized as
Physical biometrics: This involves some form of physical
measurement and includes modalities such as face,
fingerprints, iris-scans, hand geometry etc.
Behavioral biometrics: These are usually temporal in nature
and involve measuring the way in which a user performs
certain tasks. This includes modalities such as speech,
signature, gait, keystroke dynamics etc.
International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE) ISSN: 0976-1353 Volume 14 Issue 2 –APRIL 2015
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Chemical biometrics: This is still a nascent field and involves
measuring chemical cues such as odor and the chemical
composition of human perspiration.
It is also instructive to compare the relative merits and de-
merits of biometric and password/cryptographic key based systems. Table 1.1 provides a summary of them. Depending
on the application, biometrics can be used for identification or
for verification. In verification, the biometric is used to
validate the claim made by the individual. The biometric of
the user is compared with the biometric of the claimed
individual in the database. The claim is rejected or accepted
based on the match. (In essence, the system tries to answer the
question, “Am I whom I claim to be?”). In identification, the
system recognizes an individual by comparing his biometrics
with every record in the database.
Table1.1: Comparison of Biometric and Password/Key based authentication[12].
Biometrics Authentication Password/Key based
authentication
Based on physiological
measurements or behavioral
traits
Based on something that the
use has or knows
Authenticates the user Authenticates the
password/key
Is permanently associated
with the user
Can be lent, lost or stolen
Biometric templates have
high uncertainty
Have zero uncertainty
Utilizes probabilistic
matching
Requires exact match for
authentication
1.2 Biometrics and Pattern Recognition
As recently as a decade ago, biometrics did not exist as a
separate field. It has evolved through interaction and
confluence of several fields. Fingerprint recognition[3]
emerged from the application of pattern recognition to
forensics. Speaker verification evolved out of the signal
processing community. Face detection and recognition was
largely researched by the computer vision community. While
biometrics is primarily considered as application of pattern
recognition techniques, it has several outstanding differences
from conventional classification problems as enumerated
below:
1. In a conventional pattern classification problem such
as Optical Character Recognition (OCR) recognition,
the number of patterns to classify is small (A-Z)
compared to the number of samples available for
each class. However in case of biometric recognition,
the number of classes is as large as the set of
individuals in the database. Moreover, it is very
common that only a single template is registered per
user.
2. The primary task in biometric recognition is that of
choosing a proper feature representation. Once the
features are carefully chosen, the act of performing
verification is fairly straightforward and commonly
employs simple metrics such as Euclidean distance.
Hence the most challenging aspects of biometric identification involve signal and image processing for
feature extraction.
3. Since biometric templates represent personally
identifiable information of individuals, security and
privacy of the data is of particular importance unlike
other applications of pattern recognition.
4. Modalities such as fingerprints, where the template is
expressed as an unordered point set (minutiae) do not
fall under the category of traditional multi-
variate/vectorial features commonly used in pattern
recognition.
1.3 Fingerprints as Biometric
Fingerprints were accepted formally[1] as valid
personal identifier in the early twentieth century
and have since then become a de-facto authentication
technique in law-enforcement agencies worldwide.
The FBI currently maintains more than 400 million
fingerprint records on file.
Fingerprints have several advantages over other
biometrics, such as the following:
1. High universality: A large majority of the human
population has legible fingerprints and can therefore be easily authenticated. This exceeds the extent of the
population who possess passports, ID cards or any
other form of tokens.
2. High distinctiveness: Even identical twins who share
the same DNA have been shown to have different
fingerprints, since the ridge structure on the finger is
not encoded in the genes of an individual. Thus,
fingerprints represent a stronger authentication
mechanism than DNA. Furthermore, there has been
no evidence of identical fingerprints in more than a
century of forensic practice. There are also
mathematical models that justify the high distinctiveness of fingerprint patterns.
3. High permanence: The ridge patterns on the surface
of the finger are formed in the womb and remain
invariant until death except in the case of severe
burns or deep physical injuries.
4. Easy collectability: The process of collecting
fingerprints has become very easy with the advent of
online sensors. These sensors are capable of
capturing high resolution images of the finger surface
within a matter of seconds. This process requires
minimal or no user training and can be collected easily from co-operative or non co-operative users. In
contrast, other accurate modalities like iris
recognition require very co-operative users and have
considerable learning curve in using the identification
system.
International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE) ISSN: 0976-1353 Volume 14 Issue 2 –APRIL 2015
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5. High performance: Fingerprints remain one of the
most accurate biometric modalities available to date
with jointly optimal FAR (false accept rate) and FRR
false reject rate). Forensic systems are currently
capable of achieving FAR of less than 10-4.
6. Wide acceptability: While a minority of the user population is reluctant to give their fingerprints due
to the association with criminal and forensic
fingerprint databases, it is by far the most widely
used modality for biometric authentication.
A fingerprint is the feature pattern of one finger (Figure 1.1).
It is believed with strong evidences that each fingerprint is
unique. Each person has his own fingerprints with the
permanent uniqueness. So fingerprints have being used for
identification and forensic investigation for a long time.
Fig 1.1 A fingerprint image acquired by an Optical Sensor
A fingerprint is composed of many ridges and furrows. These
ridges and furrows present good similarities in each small
local window, like parallelism and average width.
However, shown by intensive research on fingerprint
recognition, fingerprints are not distinguished by their ridges
and furrows, but by Minutia, which are some abnormal points
on the ridges (Figure 1.2). Among the variety of minutia types reported in literatures, two are mostly significant and in heavy
usage: one is called termination, which is the immediate
ending of a ridge; the other is called bifurcation, which is the
point on the ridge from which two branches derive.
Fig 1.2 Minutia.[2] (Valley is also referred as Furrow, Termination
also called Ending,
and Bifurcation is also called Branch)
Fingerprinting has been around for a long period of time.
Police departments have been using fingerprinting to identify
criminals. The advancement in technology has allowed for the digitizing of fingerprints. There are devices that emulate the
methods of the police by matching minutiae – the ridges and
swirls on the bottom of a person‟s finger.
Is this person authorized to enter this facility? Is this
individual entitled to access privileged information? Is the
given service being administered exclusively to the enrolled
users? Answers to questions such as these are valuable to
business and government organizations. Because biometric
identifiers cannot be easily misplaced, forged, or shared, they
are considered more reliable for person recognition than
traditional token- or knowledge-based methods.
The objectives of biometric recognition are user convenience,
better security (e.g., difficult to forge access), and higher efficiency. The tremendous success of fingerprint based
recognition technology in law enforcement applications,
decreasing cost of fingerprint sensing devices, increasing
availability of inexpensive computing power, and growing
identity fraud/theft have all ushered in an era of fingerprint-
based person recognition applications in commercial, civilian,
and financial domains. There is a popular misconception in the
pattern recognition and image processing academic
community that automatic fingerprint recognition is a fully
solved problem in as much as it was one of the first
applications of machine pattern recognition almost fifty years
ago. On the contrary, fingerprint recognition is still a challenging and important pattern recognition problem.
1.4 History of Fingerprints
Human fingerprints have been discovered on a large number
of archaeological artifacts and historical items. Although these
findings provide evidence to show that ancient people were
aware of the individuality of fingerprints, such awareness does
not appear to have any scientific basis (Lee and Gaensslen
(2001) and Moenssens (1971)). It was not until the late
sixteenth century that the modern scientific fingerprint
technique was first initiated (see Cummins and Midlo (1961),
Galton (1892), and Lee and Gaensslen (2001)). [2][3].
Fig 1.3 Archeological finger prints
Fig 1.3 gives examples of archaeological fingerprint carvings
and historic fingerprint impressions: a) Neolithic carvings b)
standing stone c) a Chinese clay seal (300 B.C.) d) an
impression on a Palestinian lam(400 A.D.)
Fingerprint recognition problem can be grouped into two sub-
domains: one is fingerprint verification and the other is fingerprint identification (Figure 1.1.2). In addition, different
from the manual approach for fingerprint recognition by
experts, the fingerprint recognition here is referred as AFRS
(Automatic Fingerprint Recognition System), which is
program-based.
International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE) ISSN: 0976-1353 Volume 14 Issue 2 –APRIL 2015
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Fig 1.4 Verification vs. Identification
Fingerprint verification is to verify the authenticity of one
person by his fingerprint. The user provides his fingerprint
together with his identity information like his ID number. The fingerprint verification system retrieves the fingerprint
template according to the ID number and matches the template
with the real-time acquired fingerprint from the user. Usually
it is the underlying design principle of AFAS (Automatic
Fingerprint Authentication System). Fingerprint identification
is to specify one person‟s identity by his fingerprint(s).
Without knowledge of the person‟s identity, the fingerprint
identification system tries to match his fingerprint(s) with
those in the whole fingerprint database. It is especially useful
for criminal investigation cases. And it is the design principle
of AFIS (Automatic Fingerprint Identification System). However, all fingerprint recognition problems, either
verification or identification, are ultimately based on a well-
defined representation of a fingerprint. As long as the
representation of fingerprints remains the uniqueness and
keeps simple, the fingerprint matching, either for the 1-to-1
verification case or 1-to-m identification case, is
straightforward and easy.
Two representation forms for fingerprints separate the two
approaches for fingerprint recognition. The first approach,
which is minutia-based, represents the fingerprint by its local
features, like terminations and bifurcations. This approach has
been intensively studied, also is the backbone of the current available fingerprint recognition products. We concentrate on
this approach in our project.
The second approach, which uses image-based methods [6][7],
tries to do matching based on the global features of a whole
fingerprint image. It is an advanced and newly emerging
method for fingerprint recognition. And it is useful to solve
some intractable problems of the first approach. But my
project does not aim at this method, so further study in this
direction is not expanded in my thesis.
1.5 Biometric Systems
A biometric system[1] is essentially a pattern recognition system that recognizes a person by determining the
authenticity of a specific physiological and/or behavioral
characteristic possessed by that person. An important issue in
designing a practical biometric system is to determine how an
individual is recognized. Depending on the application
context, a biometric system may be called either a verification
system or an identification system:
• A verification system authenticates a person‟s identity by
comparing the captured biometric characteristic with her own
biometric template(s) pre-stored in the system. It conducts one-to-one comparison to determine whether the identity
claimed by the individual is true. A verification system either
rejects or accepts the submitted claim of identity (Am I whom
I claim I am?).
• An identification system recognizes an individual by
searching the entire template database for a match. It conducts
one-to-many comparisons to establish the identity of the
individual. In an identification system, the system establishes
a subject‟s identity (or fails if the subject is not enrolled in the
system database) without the subject having to claim an
identity (Who am I?).
The term authentication is also frequently used in the biometric field, sometimes as a synonym for verification;
actually, in the information technology language,
authenticating a user means to let the system know the user
identity regardless of the mode (verification or identification).
Throughout this book we use the generic term recognition
where we are not interested in distinguishing between
verification and identification.
The block diagrams of a verification system and an
identification system are depicted in Figure 1.5; user
enrollment, which is common to both tasks is also graphically
illustrated. The enrollment module is responsible for registering individuals in the biometric system database
(system DB). During the enrollment phase, the biometric
characteristic of an individual is first scanned by a biometric
reader to produce a raw digital representation of the
characteristic. A quality check is generally performed to
ensure that the acquired sample can be reliably processed by
successive stages. In order to facilitate matching, the raw
digital representation is usually further processed by a feature
extractor to generate a compact but expressive representation,
called a template. Depending on the application, the template
may be stored in the central database of the biometric system
or be recorded on a magnetic card or smartcard issued to the individual.
The verification task is responsible for verifying individuals at
the point of access. During the operation phase, the user‟s
name or PIN (Personal Identification Number) is entered
through a keyboard (or a keypad); the biometric reader
captures the characteristic of the individual to be recognized
and converts it to a digital format, which is further processed
by the feature extractor to produce a compact digital
representation. The resulting representation is fed to the
feature matcher, which compares it against the template of a
single user (retrieved from the system DB based on the user‟s PIN). In the identification task, no PIN is provided and the
system compares the representation of the input biometric
against the templates of all the users in the system database;
the output is either the identity of an enrolled user or an alert
message such as “user not identified.”
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Because identification in large databases is computationally
expensive, classification and indexing techniques are often
deployed to limit the number of templates that have to be
matched against the input.
These applications may be divided into the following groups:
i) applications such as banking, electronic commerce, and access control, in which biometrics will replace or enforce the
current token- or knowledge-based techniques and ii)
applications such as welfare and immigration in which neither
the token-based nor the knowledge-based techniques are
currently being used.
Two approaches for Fingerprint recognition
Two representation forms for fingerprints separate the two
approaches[4] for fingerprint recognition. The first approach,
which is minutia-based, represents the fingerprint by its local
features, like terminations and bifurcations. This approach has
been intensively studied, also is the backbone of the current
available fingerprint recognition products. I also concentrate on this approach in my honors project.
The second approach, which uses image-based methods, tries
to do matching based on the global features of a whole
fingerprint image. It is an advanced and newly emerging
method for fingerprint recognition. And it is useful to solve
some intractable problems of the first approach. But my
project does not aim at this method, so further study in this
direction is not expanded in my thesis.
1.7 Applications of Fingerprint Recognition Systems
Fingerprint recognition is a rapidly evolving technology that
has been widely used in forensics such as criminal recognition and prison security, and has a very strong potential to be
widely adopted in a broad range of civilian applications[5]
Forensic Government Commercial
Corpse
identification,
Criminal
Investigation,
Terrorist
Identification,
Parenthood
Determination,
Missing
Children,etc
National ID card,
Correctional
Facility,
Driver’s License
Social Security,
Welfare
Disbursement,
Border Control,
Passport
Control,etc
Computer
Network Logon,
Electronic Data
Security,
E-Commerce,
Internet Access,
ATM, Credit
Card,
Physical Access
Control,
Cellular Phones,
Personal Digital
Assistant,
Medical Records
Management,
Distance
Learning, etc
Table1.2 Most of the fingerprint recognition applications are
divided here into three categories.
Traditionally, forensic applications have used manual
biometrics, government applications have used token-based
systems, and commercial applications have used knowledge-
based systems. Fingerprint recognition systems are now being
increasingly used for all these sectors.
Note that over one billion dollars in welfare benefits are
annually claimed by “double dipping” welfare recipients in the
United States alone. These applications may be divided into the following groups: i) applications such as banking,
electronic commerce, and access control, in which biometrics
will replace or enforce the current token- or knowledge-based
techniques and ii) applications such as welfare and
immigration in which neither the token-based nor the
knowledge-based techniques are currently being used.
Information system/computer network security, such as user
authentication and access to databases via remote login, is one
of the most important application areas for fingerprint
recognition. It is expected that more and more information
systems/computer networks will be secured with fingerprints with the rapid expansion of the Internet. Applications such as
medical information systems, distance learning and e-
publishing are already benefiting from deployment of such
systems. Electronic commerce and electronic banking are also
important and emerging application areas of biometrics due to
the rapid progress in electronic transactions. These
applications include electronic fund transfers, ATM security,
check cashing, credit card security, smartcard security, on-line
transactions, and so on. Currently, there are several large
fingerprint security projects under development in these areas,
including credit card security (MasterCard) and smartcard
security (IBM and American Express). The physical access control market is currently dominated by token-based
technology. However, it is increasingly shifting to fingerprint-
based biometric techniques. The introduction of fingerprint-
based biometrics in government benefits distribution programs
such as welfare disbursement has already resulted in
substantial savings in deterring multiple claimants. In addition,
customs and immigration initiatives such as the INS Passenger
Accelerated Service System (INSPASS) which permits faster
immigration procedures based on hand geometry will greatly
increase operational efficiency. Fingerprint-based national ID
(Aadhar) systems provide a unique ID to the citizens and integrate different government services. Fingerprint-based
voter and driver registration provides registration facilities for
voters and drivers. Fingerprint-based time/attendance
monitoring systems can be used to prevent any abuses of the
current token based/ manual systems. Fingerprint-based
recognition systems will replace passwords and tokens in a
large number of applications. Their use will increasingly
reduce identity theft and fraud and protect privacy. As
International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE) ISSN: 0976-1353 Volume 14 Issue 2 –APRIL 2015
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fingerprint technology matures, there will be increasing
interaction among market, technology, and applications. The
emerging interaction is expected to be influenced by the added
value of the technology, the sensitivities of the user
population, and the credibility of the service provider. It is too
early to predict where and how fingerprint technology would evolve and be mated with which applications, but it is certain
that fingerprint-based recognition will have a profound
influence on the way we will conduct our daily business.
II LITERATURE SURVEY
In the field of fingerprint identification, different types of
work have been done so far. We had gone through various
research papers, the work done till today and the methods used
in each work are shown under this section
Fast Fourier Transform and Gabor Filters
The paper [18] uses a combination of two algorithms, the Fast
Fourier Transform and gabor Filters to enhance and
reconstruct the image‟s information. The system consists of eight steps: Acquisition, Noise reduction and enhancement
with gabor filters. Enhancement with Fast Fourier Transform,
Binarization, Thinning, Minutia detection and recognition.
This is used to enhance and reconstruct the information of the
finger print image as well as to extract two fundamental types
of minutiae, ending points and bifurcations. Finally the
extracted features are used to perform the fingerprint
recognition. In this method the image is divided into small
processing blocks and then fourier transform is performed.
Minutiae-Based Algorithms Using CLAHE
Histogram equalization is a general process used to enhance the contrast of images by transforming its intensity values . As
a secondary result, it can amplify the noise producing worse
results than the original image for certain fingerprints.
Therefore, instead of using the histogram equalization which
affects the whole image,[17] uses CLAHE (contrast limited
adaptive histogram equalization) which is applied to enhance
the contrast of small tiles and to combine the neighboring tiles
in an image by using bilinear interpolation, which eliminates
the artificially induced boundaries. In addition, the 'Clip Limit'
factor is applied to avoid over-saturation of the image
specifically in homogeneous areas that present high peaks in the histogram of certain image tiles due to many pixels falling
inside the same gray level range . Additionally, a combination
of filters in both domains, spatial and Fourier is used to obtain
a proper enhanced image.
Segmentation Algorithm
[20] Proposed an algorithm for segmentation that employs
feature dots, which are then used to obtain a close
segmentation curve. The authors claim that their method
surpasses directional field and orientation based methods
Segmentation is one of the first and most integral pre-
processing steps for any fingerprint verification and it
determines the result of fingerprint analysis and recognition. Segmentation refers to the separation of finer print area from
the image background. A good segmentation method should
have the following characteristics:
1. It should be insensitive to image contrast.
2.It should detect the smudged or noisy regions.
3.It should be independent of the image quality.
Thinning Algorithm
The paper [19] uses a modified thinning algorithm is that can
be used to thin any symbol, Characters and also fingerprint images regardless of their shape and orientation. Usually in
fingerprint images, Thinning seen as a preprocess for minutiae
extraction. The Proposed algorithm identifies the
unrecoverable corrupted areas in the fingerprint and does not
thin them; this is an important advantage of the proposed
method because such corrupted areas are extremely harmful to
the extraction of minutiae points. Moreover, this advantage
helps remove the spurious minutiae points which are harmful
to fingerprint matching.
III REQUIREMENTS ANALYSIS
Software requirements
Operating System - Windows XP/7
Tool – MATLAB R2010b
Visual basic 2008
SQL server
Hardware Requirements
PC with Processor - Pentium 4 or Higher
PC RAM – 2 GB or Higher
Hard Disk Drive – 40GB or Higher
Fingerprint scanner
IV PROPOSED SYSTEM
4.1 System Overview The proposed system provides solution to lecture attendance
problems through the use of attendance management software
that is interfaced to a fingerprint device. The staff bio-data
(Matriculation number, Name, Gender and Date of Birth) and
the fingerprint are enrolled first into the database. The
fingerprint is captured using a fingerprint scanner device.
For attendance, the staff places his/ her finger over the
fingerprint device and the staff‟s matriculation number is sent
to the database as having attended that particular working day.
At the end of every month, reports are generated and sent to
mail ids of all the staff. A simple architecture is shown below.
Fingerprint
input
Biometric
sensor
Feature
Extraction
Database
Matching
Feature
Extraction Biometric
sensor
Fingerprint
input
International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE) ISSN: 0976-1353 Volume 14 Issue 2 –APRIL 2015
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Fig 4.1 General Architecture of a Biometric System
4.2 System Level Design
An Automated Fingerprint Attendance System (AFAS) is a
highly specialized system that records staff attendance by
comparing a single fingerprint image with the fingerprint
images previously stored in a database. The Automated
Fingerprint Identification system (AFIS) is the principle behind the AFAS.
The major factors in designing a fingerprint attendance system
include: choosing the hardware and software components and
integrating both to work together, defining the system working
mode (verification or identification), dealing with poor quality
images and other programming language exception, and
defining administration and optimization policy [5],[9].
Staff attendance system framework is divided into three parts:
Hardware design, Software design, Attendance Management
Approach and Report Generation.
A fingerprint recognition system constitutes of fingerprint
acquiring device, minutia extractor and minutia matcher [Figure 4.2].
Fig 4.2 Simplified Fingerprint Recognition System
For fingerprint acquisition, optical or semi-conduct sensors are
widely used. They have high efficiency and acceptable
accuracy except for some cases that the user‟s finger is too
dirty or dry. However, the testing database for my project is
from the available fingerprints provided by FVC2002
(Fingerprint Verification Competition 2002). So no acquisition
stage is implemented.
The minutia extractor and minutia matcher modules are
explained in detail in the next part for algorithm design and other subsequent sections.
Hardware
The hardware to be used can be divided into two categories –
fingerprint scanner which captures the image and a personal
computer which: houses the database, runs the comparison
algorithm and simulates the application function. The
fingerprint scanner is connected to the computer via its USB
interface. Basically this work does not involve the
development of hardware. Using the Secugen Fingerprint
Reader, the GrFinger Software Development Kit (SDK)
toolbox provided by the Griaule (will explain the detail) can
be used as an interface between the fingerprint reader and the attendance software.
Fig 4.3 Fingerprint Device.
Software
The software architecture consists of: the database and the
application program:-
Database: The database consists of tables that stores records
implemented in Microsoft SQL Server database. However,
this can be migrated to any other relational database of choice.
SQL Server is fast and easy, it can store a very large record
and requires little configuration.
Application Program: The application program used is
MATLAB.
V FINGERPRINT IMAGE PREPROCESSING
5.1 Fingerprint Image Enhancement
Fingerprint Image enhancement[16][17] is to make the image
clearer for easy further operations. Since the fingerprint
images acquired from sensors or other medias are not assured
with perfect quality, those enhancement methods, for
increasing the contrast between ridges and furrows and for
connecting the false broken points of ridges due to insufficient
amount of ink, are very useful for keep a higher accuracy to
fingerprint recognition. Two Methods are adopted in my fingerprint recognition
system: the first one is Histogram Equalization; the next one is
Fourier Transform.
5.1.1 Histogram Equalization
Histogram equalization[17] is to expand the pixel value
distribution of an image so as to increase the perceptional
information. The original histogram of a fingerprint image has
the bimodal type [Figure 5.1], the histogram after the
histogram equalization occupies all the range from 0 to 255
and the visualization effect is enhanced [Figure 5.2].
Figure 5.1.1 the Original histogram
of a fingerprint image
Figure 5.1.2 Histogram after the
Histogram Equalization
International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE) ISSN: 0976-1353 Volume 14 Issue 2 –APRIL 2015
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The right side of the following figure [Figure 5.3] is the output
after the histogram equalization.
Fig 5.3 Histogram Enhancement.Original Image (Left). Enhanced
image (Right)
5.1.2 Fingerprint Enhancement by Fourier Transform
We divide the image into small processing
block s (32 by 32 pixels) and perform the Fourier transform[16] according to:
F(u,v)=
+ )}
For u = 0, 1, 2, ..., 31 and v = 0, 1, 2, ..., 31.
In order to enhance a specific block by its dominant
frequencies, we multiply the FFT of the block by its
magnitude a set of times. Where the magnitude of the original
FFT = abs(F(u,v)) = |F(u,v)|.
Get the enhanced block according to
ˆk} (2)
where F-1(F(u,v)) is done by
F(u,v)=
+
)} (3)
for x = 0, 1, 2, ..., 31 and y = 0, 1, 2, ..., 31.
The k in formula (2) is an experimentally determined constant,
which we choose k=0.45 to calculate. While having a higher
"k" improves the appearance of the ridges, filling up small
holes in ridges, having too high a "k" can result in false
joining of ridges. Thus a termination might become a bifurcation. Figure 5.4 presents the image after FFT
enhancement.
Fig 5.4 Fingerprint enhancements by FFT Enhanced image (left),
Original image (right)
The enhanced image after FFT has the improvements to
connect some falsely broken points on ridges and to remove
some spurious connections between ridges. The shown image
at the left side of figure 5.4 is also processed with histogram
equalization after the FFT transform. The side effect of each block is obvious but it has no harm to the further operations
because I find the image after consecutive binarization
operation is pretty good as long as the side effect is not too
severe.
5.2 Fingerprint Image Binarization
Fingerprint Image Binarization[16] is to transform the 8-bit
Gray fingerprint image to a 1-bit image with 0-value for ridges
and 1-value for furrows. After the operation, ridges in the
fingerprint are highlighted with black color while furrows are
white.
A locally adaptive binarization method is performed to
binarize the fingerprint image. Such a named method comes from the mechanism of transforming a pixel value to 1 if the
value is larger than the mean intensity value of the current
block (16x16) to which the pixel belongs [Figure 5.5].
Fig 5.5 The Fingerprint image after adaptive binarization, Binarized
image(left), Enhanced gray image(right)
5.3 Fingerprint Image Segmentation
In general, only a Region of Interest (ROI) is useful to be
recognized for each fingerprint image. The image area without
effective ridges and furrows is first discarded since it only
holds background information. Then the bound of the
remaining effective area is sketched out since the minutia in
the bound region is confusing with those spurious minutias
that are generated when the ridges are out of the sensor.
To extract the ROI, a two-step method is used. The first step is
block direction estimation and direction variety check [15], while the second is intrigued from some Morphological
methods.
Block direction estimation
1.1 Estimate the block direction for each block of the
fingerprint image with WxW in size(W is 16 pixels by
default). The algorithm is:
I. Calculate the gradient values along x-direction (gx)
and y-direction (gy) for each pixel of the block. Two
Sobel filters are used to fulfill the task.
II. For each block, use following formula to get the
Least Square approximation of the block direction.
tg2ß = 2 (gx*gy)/(gx2-gy2) for all the pixels
in each block.
The formula is easy to understand by regarding gradient
values along x-direction and y-direction as cosine value and
International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE) ISSN: 0976-1353 Volume 14 Issue 2 –APRIL 2015
910
sine value. So the tangent value of the block direction is
estimated nearly the same as the way illustrated by the
following formula.
tg2= 2sincos/(cos2 -sin2 ) 1.2 After finished with the estimation of each block direction,
those blocks without significant information on ridges and
furrows are discarded based on the following formulas:
E = {2 (gx*gy)+(gx2-gy2)}/W*W*(gx2+gy2)
For each block, if its certainty level E is below a threshold, then the block is regarded as a background block. The
direction map is shown in the following diagram. We assume
there is only one fingerprint in each image.
Fig 5.6 Direction map. Binarized fingerprint (left), Direction
map (right)
ROI extraction by Morphological operations
Two Morphological operations called „OPEN‟ and „CLOSE‟
are adopted[15]. The „OPEN‟ operation can expand images and remove peaks introduced by background noise [Figure
5.7]. The „CLOSE‟ operation can shrink images and eliminate
small cavities [Figure 5.8].
Figure 5.7,5.8,5.9,5.10 show the interest fingerprint image
area and its bound. The bound is the subtraction of the closed
area from the opened area. Then the algorithm throws away
those leftmost, rightmost, uppermost and bottommost blocks
out of the bound so as to get the tightly bounded region just
containing the bound and inner area.
Fig 5.7 Original Image Area Fig 5.8 After close operation
Fig 5.9 After open operation Fig5.9 ROI+Bound
VI MINUTIA EXTRACTION
6.1 Fingerprint Ridge Thinning
Ridge Thinning is to eliminate the redundant pixels of ridges
till the ridges are just one pixel wide. [12] [14]uses an
iterative, parallel thinning algorithm. In each scan of the full
fingerprint image, the algorithm marks down redundant pixels
in each small image window (3x3). And finally removes all
those marked pixels after several scans. In my testing, such an iterative, parallel thinning algorithm has bad efficiency
although it can get an ideal thinned ridge map after enough
scans. [2] uses one-in-all method to extract thinned ridges
from gray-level fingerprint images directly. Their method
traces along the ridges having maximum gray intensity value.
However, binarization is implicitly enforced since only pixels
with maximum gray intensity value are remained. Also in my
testing, the advancement of each trace step still has large
computation complexity although it does not require the
movement of pixel by pixel as in other thinning algorithms.
Thus the third method is bid out which uses the built-in
Morphological thinning function in MATLAB.
6.2 Minutia Marking
After the fingerprint ridge thinning, marking minutia points is
relatively easy. But it is still not a trivial task as most
literatures declared because at least one special case evokes
my caution during the minutia marking stage.
In general, for each 3x3 window, if the central pixel is 1 and
has exactly 3 one-value neighbors, then the central pixel is a
ridge branch [Figure 4.2.1]. If the central pixel is 1 and has
only 1 one-value neighbor, then the central pixel is a ridge
ending[14] [Figure4.2.2].
Fig 6.1: Bifurcation Fig 6.2: Termination
Figure 6.3 Triple counting branches
0 0 0
0 1 0
0 0 1
0 1 0
0 1 0
1 0 1
0 1 0
0 1 1
1 0 0
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Figure 6.3 illustrates a special case that a genuine branch is
triple counted. Suppose both the uppermost pixel with value 1
and the rightmost pixel with value 1 have another neighbor
outside the 3x3 window, so the two pixels will be marked as
branches too. But actually only one branch is located in the
small region. So a check routine requiring that none of the neighbors of a branch are branches is added.
Also the average inter-ridge width D is estimated at this stage.
The average inter-ridge width refers to the average distance
between two neighboring ridges. The way to approximate the
D value is simple. Scan a row of the thinned ridge image and
sum up all pixels in the row whose value is one. Then divide
the row length with the above summation to get an inter-ridge
width. For more accuracy, such kind of row scan is performed
upon several other rows and column scans are also conducted,
finally all the inter-ridge widths are averaged to get the D.
Together with the minutia marking, all thinned ridges in the
fingerprint image are labeled with a unique ID for further operation. The labeling operation is realized by using the
Morphological operation: BWLABEL.
VII MINUTIA MATCH
Given two set of minutia of two fingerprint images, the minutia match algorithm determines whether the two minutia
sets are from the same finger or not.
An alignment-based match algorithm partially derived from
the [1] is used in my project. It includes two consecutive
stages: one is alignment stage and the second is match stage.
1. Alignment stage. Given two fingerprint images to be
matched, choose any one minutia from each image;
calculate the similarity of the two ridges associated with
the two referenced minutia points. If the similarity is
larger than a threshold, transform each set of minutia to
a new coordination system whose origin is at the
referenced point and whose x-axis is coincident with the direction of the referenced point.
2. Match stage: After we get two set of transformed
minutia points, we use the elastic match algorithm to
count the matched minutia pairs by assuming two
minutia having nearly the same position and direction
are identical.
7.1 Alignment Stage
1. The ridge associated with each minutia is represented as a
series of x-coordinates (x1, x2…xn) of the points on the ridge.
A point is sampled per ridge length L starting from the minutia point, where the L is the average inter-ridge length. And n is
set to 10 unless the total ridge length is less than 10*L.
So the similarity of correlating the two ridges is derived from:
S = mi=0xiXi/[
mi=0xi
2Xi2]^0.5,
where(xi~xn) and (Xi~XN) are the set of minutia for each
fingerprint image respectively. And m is minimal one of the n
and N value. If the similarity score is larger than 0.8, then go
to step 2, otherwise continue to match the next pair of ridges.
2. For each fingerprint, translate and rotate all other minutia
with respect to the reference minutia according to the
following formula:
xi_new
yi_new
i_new
xi x( )
yi y( )
i
=TM *
,
where (x,y,) is the parameters of the reference minutia, and TM is
TM =
cos
sin
0
sin
cos
0
0
0
1
Fig 7.1: the effect of translation and rotation
The new coordinate system is originated at minutia F and the
new x-axis is coincident with the direction of minutia F. No
scaling effect is taken into account by assuming two
fingerprints from the same finger have nearly the same
size.My method to align two fingerprints is almost the same
with the one used by [1] but is different at step 2. Lin‟s
method uses the rotation angle calculated from all the sparsely
sampled ridge points. My method use the rotation angle
calculated earlier by densely tracing a short ridge start from
the minutia with length D. Since I have already got the minutia direction at the minutia extraction stage, obviously my
method reduces the redundant calculation but still holds the
accuracy.
Also Lin‟s way to do transformation is to directly align one
fingerprint image to another according to the discrepancy of
the reference minutia pair. But it still requires a transform to
the polar coordinate system for each image at the next minutia
match stage. My approach is to transform each according to its
own reference minutia and then do match in a unified x-y
coordinate. Therefore, less computation workload is achieved
through my method.
7.2 Match Stage
The matching algorithm for the aligned minutia patterns needs
to be elastic since the strict match requiring that all parameters
(x, y, ) are the same for two identical minutia is impossible due to the slight deformations and inexact quantizations of
minutia.
My approach to elastically match minutia is achieved by
placing a bounding box around each template minutia. If the
minutia to be matched is within the rectangle box and the
direction discrepancy between them is very small, then the
two minutia are regarded as a matched minutia pair. Each
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912
minutia in the template image either has no matched minutia
or hasonly one corresponding minutia.
The final match ratio for two fingerprints is the number of
total matched pair over the number of minutia of the template
fingerprint. The score is 100*ratio and ranges from 0 to 100. If
the score is larger than a pre-specified threshold, the two fingerprints are from the same finger. However, the elastic
match algorithm has large computation complexity and is
vulnerable to spurious minutia.
VIII IMPLEMENTATION
USER MANUAL
1. Type command start_gui_single_mode in MATLAB
Figure M.1 the User Interface of the Fingerprint Recognition System. The series of buttons on the left side will be invoked
sequentially in the consequent demonstration. The two blank
areas are used to show the fingerprint image before and after a transaction respectively.
2.Click Load Button
Figure M.2 Load a gray level fingerprint image from a drive specified by
Users. Multiple formats are supported and the image size is not
limited. But the fingerprint ridges should have large gray
intensity comparing with the background and valleys.
3. Click his-Equalization Button
Figure M.3 After Histogram Equalization.
The image on the left side is the original fingerprint.
The enhanced image after the Histogram
Equalization is shown on the right side.
4. Click fft Button
Figure M.4 Captured window after click „FFT‟ button. The pop-up dialog accepts the parameter k (please refer
the formula 2). The experimental optimal k value is 0.45.
Users can fill any other constant in the dialog to get a
International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE) ISSN: 0976-1353 Volume 14 Issue 2 –APRIL 2015
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better performance. The enhanced image will be shown in
the left screen box, which however is not shown here.
5. Click Binarization Button
6. Click Direction Button
Figure M.5. Screen capture after binarization (left) and block direction
estimation (right).
7. Click ROI Area Button
Figure M.7 ROI extraction(right).
The intermediate steps for all the morphological operations
such close and open are not shown. The right screen box
shows the final region of interest of the fingerprint image. The subsequent operations will only operate on the region of
interest.
8. Click Thinning Button
9. Click Remove H breaks Button
10. Click Remove spikes Button
Figure M.10 the Fingerprint image after thining, H breaks removal, isolated
peaks removal and spike removal.(right).
11. Click Extract Button
12. Click Real Minutia Button
Figure M.12 Minutia Marking (right) and False Minutia Removal (Left). Bifurcations are located with yellow crosses and terminations
are denotes with red stars. And the genuine minutia (left) are
labeled with orientations with green arrows.
13. Click Save Button
Figure M.13 Save minutia to a text file.
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The saved text file stores the information on all genuine
minutia. The exact format of the files are explained in the
source code.
14. Click Match Button
Figure M.14 Load two minutia files and do matching. Users can open two minutia data files from the dialog invoked
by clicking the „Match‟ button. The match algorithm will
return a prompt of the match score. But be noted that matching
in the GUI mode is not encouraged since the match algorithm
relies on heavy computation. Unpredicted states will happen
after a long irresponsive running time. Batch testing is
prepared for testing match. Please refer the source files for batch testing.
IX EXPERIMENTATION RESULTS
9.1 Evaluation Indexes for Fingerprint Recognition
Two indexes are well accepted to determine the performance
of a fingerprint recognition system: one is FRR (false rejection
rate) and the other is FAR (false acceptance rate). For an
image database, each sample is matched against the remaining
samples of the same finger to compute the False Rejection
Rate. If the matching g against h is performed, the symmetric
one (i.e., h against g) is not executed to avoid correlation. All the scores for such matches are composed into a series of
Correct Score. Also the first sample of each finger in the
database is matched against the first sample of the remaining
fingers to compute the False Acceptance Rate. If the matching
g against h is performed, the symmetric one (i.e., h against g)
is not executed to avoid correlation. All the scores from such
matches are composed into a series of Incorrect Score.
9.2 Experimentation Results
A fingerprint database from the FVC2000 (Fingerprint
Verification Competition 2000) is used to test the experiment performance. My program tests all the images without any
fine-tuning for the database. I have taken 100 inputs for
checking the false accept rate and false reject rate. The
accuracy of the system is quantified in terms of false
acceptance ratio (FAR) and the false rejection ratio (FRR). An
FAR of 1% was obtained for an FRR of 8% for this database.
Table of FAR and FRR
Number
of inputs
FAR FRR
100
1%
8%
35
40
45
50
55
image 1 image 2 image 3 image 4 image 5
Figure 9.1: Graph of image degradation v/s percentage of matching
Above graph shows the degradation of single fingerprint v/s
percentage of matching.
0%
50%
100%
150%
0 10 20 30 40
Y-Values
Figure9.2: graph of threshold value v/s system efficiency
The above figure 9.2 shows graph of threshold values v/s the
system efficiency. System will be more efficient at the
threshold value 26.
38
40
42
44
46
48
50
image 1image 2image 3image 4image 5image 6
Figure9.3: graph of different fingerprint images v/s percentage of matching
The above figure 9.3 shows the graph of different fingerprint
images v/s percentage of matching.
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X CONCLUSION AND FUTURE WORK
12.1 Conclusion
We have combined many methods to build a minutia extractor and a minutia matcher. The combination of multiple methods
comes from a wide investigation into research paper. Also
some novel changes like segmentation using Morphological
operations, minutia marking with special considering the triple
branch counting, minutia unification by decomposing a branch
into three terminations, and matching in the unified x-y
coordinate system after a two-step transformation are used in
my project, which are not reported in other literatures I
referred to.
Also a program coding with MATLAB going through all the
stages of the fingerprint recognition is built. It is helpful to
understand the procedures of fingerprint recognition. And demonstrate the key issues of fingerprint recognition.
12.2 Future Work
The following are the some of the interesting extensions of the
present work:
1) An investigation into a filter whose primary aim is to
specifically enhance the minutia points. This project has
followed the approach adopted by most previous work where
the emphasis is on enhancing the ridge structures using Gabor,
or Gabor-like filters. However, while the ridge structures are
enhanced, this approach has shown to be less effective in enhancing areas containing minutiae points, which are the
points of main interest.
2) The current minutiae verification algorithm is applied on
the minutiae extracted using the algorithm that detects the
minutiae in the thinned binarized fingerprint ridges. The
minutiae patterns that are learnt during the training can be
used to detect the minutiae in the gray scale fingerprint image
directly.
3) The algorithms suggested in the thesis can also be
implemented using neural networks and fuzzy logic
techniques. It is also possible to compare and analyze the
algorithms by using neural network.
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FOR MINUTIA MATCHING WITH BINARIZATION
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[20] Mrs. P.Balaramudu M.Tech1 ,J.Nageswara Rao M.Tech
Fingerprint Identification and Verification System using Minutiae Matching
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