A SECURITY BASED VOTING SYSTEMUSING BIOMETRIC

PundirManjuBaisoya Int. Journal of Engineering Research and Applications www.ijera.com
ISSN: 2248-9622, Vol. 6, Issue 3, (Part - 3) March 2016, pp.44-50
www.ijera.com 44|P a g e
A SECURITY BASED VOTING SYSTEMUSING BIOMETRIC
M.Thangamani1
, S.Shunmathy2
, S.Backiyalakshmi3
, P.T.Aiswariya4
,
K.Priyadharshini5
Computer Science and Engineering, Anna University, Chennai, India / Saranathan College of Engineering
ABSTRACT: The problem of voting is still critical in terms of safety and security. This paper is about the
design and development of a voting system using fingerprint to provide a high performance with high security
to the voting system. Fingerprint biometrics is widely used for identification. Biometrics identifiers cannot be
misplaced and they represent any individual identity. The integration of biometric with electronic voting
machine requires less manpower, save much time of voters and personal, ensure accuracy,transparency and fast
result in election. In this paper a framework for electronic voting machine based on biometric verification is
proposed and implemented. The proposed framework provides secured identification and authentication
processes for the voters and candidates through the use of fingerprint biometric
Keyword: Fingerprint, Fingerprint sensor, minutiae, Database.
I. INTRODUCTION:
Now-a-days, democracy has become an
important part of people's lives. The heart of
democracy is voting. The voting must be trust one
and vote must be recorded and tallied with
accuracy and impartiality. This is achieved by
using biometric system. An electronic voting
system defines valid voting and gives an fast
method of counting votes, which helps to yield a
final result.
Moreover, electronic voting systems can
improve voter identification process by using
biometric recognition. Biometrics is becoming an
essential personal identification solutions, since
biometric identifiers cannot be misplaced and they
represent an individual’s identity. Biometric
recognition refers to the use of iris, fingerprint,
face, palm and speech characteristics, called
biometric identifiers. Fingerprint matching is a
important for this process. It is an extremely
difficult problem, due to variations in different
impressions of the same finger. Fingerprints are
unique to each individual and they do not change
over time.
Voting system starts from the 18th
century
and many proposals for voting system have been
made till now. When designing an electronic voting
system, it is essential to consider ways in which the
voting tasks can be performed electronically
without sacrificing voter privacy or introducing
opportunities for fraud.
1.2 Requirement:
Abiometric system is a pattern recognition
system that operates by extracting biometric data
from an person, extracting a feature set from the
extracted data, and comparing this feature set
against the template set in the database. Depending
on the application, a biometric system may operate
in verification mode and identification mode.
Fingerprint biometric is the most widely publicized
biometrics for identification. This is largely due to
its easy and cost effective integration in existing
and upcoming technologies. The integration of
biometric with electronic voting machine requires
less manpower, save much time of voters and
personnel ensure accuracy, transparency and fast
results in election. In the framework for electronic
voting machine based on biometric verification is
proposed and implemented. The proposed
framework ensures secured identification and
authentication processes for the voters and
candidates through the use of fingerprint
biometrics. In this paper,using fingerprint
followingfactors are achieved,
1. Security: No one can evaluate the result before
announcement.
2. Eligibility: Only eligible voters are allow to
vote.
3. Uniqueness: voters are allow to vote only
once.
4. Accuracy: All the valid votes are automatically
calculated by the system.
5. Time consumption: The time taken to count
the vote is less than the existing system.
II. EXISTING SYSTEM
In India bar code scanning is performed
with the help of India's national ID program
called Aadhaar is the largest biometricdatabase of
the world. It is a biometrics-based digital identity,
instantly verifiable online at the point of service
(PoS), at anytime, anywhere, in a paperless way.
Currently it has 500 million people with 6 petabyte
of data.
 It will reach 1.25 billion people in few years,
15 PB of data and over 200 trillion biometric
RESEARCH ARTICLE OPEN ACCESS

matches per day.It is designed to enable
government agencies to deliver retail public
service securely based on biometric data along
with demographic data of a person.
 The data is transmitted in encrypted form over
internet for authentication, aiming to free it
from limitations of physical presence of a
person at a given place. Thus is can be used for
casting vote from anywhere, availing social
security benefits from anywhere e.g. PDS
ration form any shop etc.
Elections definesthe democracy of
people. We speak about who is allowed to
vote, how campaigns are conducted, and how
they are financed, but no one gives priority to
the understanding of the actual voting process.
Electronic Voting Machines ("EVM").
EVM consists of two units, i) Control Unit, ii)
Balloting Unit. The two units are joined by a five-
meter cable. The Control Unit is with the Presiding
Officer or a Polling Officer and the Balloting Unit
is placed inside the voting compartment.The
category “electronic voting” is potentially broad,
referring to several distinct possible stages of
electronic usage during the course of an election.
Security Problems – Many one can change the
program installed in the EVM and tamper or fraud
the results easily after the polling. By replacing a
small part of the machine we change
votepercentage of the particular candidate. These
instructions can be sent wirelessly from a mobile
phone.
Illegal Voting (Rigging) - The very
commonly known problem, Rigging which is faced
in every electoral procedure. One candidate, can
put the votes for the people without his or her
knowledge by illegally. This results in the loss of
votes for the other candidates participating and also
increases the number votes to the candidate who
performs this action. This can be done externally at
the time of voting.Traditional voting process can be
divided into different phases:
1. Identification: In this phase, voter authenticates
himself or herself by showing his or her voting
card, this step is public and verified by the
presiding officer. At the end of authentication
process, presiding officer give a ballot paper to
voter to cast his or her vote.
2. Vote: The vote takes place in a separate booth
where voter cannot be seen by any person. The
voter cast their vote by pressing the button in
machine and it will be stored.
3. Vote counting: At the end of voting time, the
presidingofficers collect the ballot box and
submit it to the counting centre. After that with
the help of members of the election committee
nominated by election commission of India,
the ballot boxes are opened and votes are
counted and the results are then announced.
4. Verification: Various types of verification
process are used, most procedure are public
and verified by the representative of candidates
of competing parties. Recount is also possible
if there is anyfraud or error.
The existing elections were done in
traditional way, using ballot, ink and tallying the
votes afterward. But this system prevents the
election from being accurate. Problems encounter
the usual elections are as follows:
• It requires human participation, in tallying the
votes that makes the elections time consuming
and prone to human error.
• The voter will be marked on the fore finger by
using ink.
• Deceitful election mechanism.
• Constant spending funds for the elections staff
every year.
III. PROPOSED SYSTEM
3.1fingerprint Recognition
Fingerprint recognition has been widely
used in both forensic and civilian applications.
Compared with other biometrics features,
fingerprint-based biometrics is the most proven
technique. In terms of applications, there are two
kinds of fingerprint recognition systems:
verification and identification.
Fingerprint Verification:Fingerprint
verification is the method where we compare the
fingerprint with an enrolled fingerprint, where our
aim is to match both the fingerprint. This method is
mainly used to verify person’sauthenticity. For
verification a person needs to his or her fingerprint
in to the fingerprint scanner. Then it is
representation is saved in some compress format
with the person’s identity and his or her name.
Then it is applied to the fingerprint verification
system so that person’s identity can be easily
verified. Fingerprint verification is also called, one
to one matching.
Fingerprint Identification:Fingerprint
identification is mainly used to specify any
person’s identify by his or her fingerprint.
Identification has been used for fingerprint
matching. Here the system matches the fingerprint
of unknown person against the other fingerprint
present in the database. This process is also called
one to many matching.
3.2 Minutiae Based Implementation
Fingerprint has been used as a method of
personal identification for over a century. It is
widely used in biometric authentication at present
because of its uniqueness and performance. A
fingerprint consists of ridges and valleys. There are

two basic features used in fingerprint recognition,
i.e. ridge endings and ridge bifurcations. Other
features are also used. According to features used
in fingerprint recognition, automatic fingerprint
recognition techniques are classified into
minutiae-based, image-based and ridge feature-
based approaches. Ridge feature-based approach
issued when minutiae are difficult to extract in very
low quality fingerprint images, whereas other
features of the fingerprint ridge pattern (e.g., local
orientation andfrequency, ridge shape, texture
information) may be extracted more reliably than
minutiae, even though
their distinctiveness is generally lower.
A fingerprint is the pattern of ridges and valleys on
the surface of a fingertip. The endpoints and
crossing points of ridges are called minutiae.
Figure1:Ridge ending and Bifurcation
3.2.1 Advantages Fingerprint Authentication
Fingerprint solutions offer many
advantages which address the human factors of
authentication.
• One of a kind identifier - Fingerprints from
each one of our ten fingers is distinctive,
different from one another and from those of
other persons. Even identical twins have
distinctive fingerprints.
• Greater convenience - Users no longer have to
remember multiple, long and complex,
frequently changing passwords or carry
multiple keys.
• Relatively equal security level for all users in a
system - One account is not easier to break into
than any other (such as an easily guessed
password or through social engineering).
• Ensures the user is present at the point and
time of recognition and later cannot deny
having accessed the system.
• Cannot be shared, lost, stolen, copied,
distributed or forgotten unlike passwords,
PINs, and smart cards. Fingerprints strongly
link an identity to a physical human being
making it difficult for attackers to forge.
• Long history of successful use in identification
tasks. Fingerprints have been used in forensics
for well over a century and there is a
substantial body of scientific studies and real
world data supporting the distinctiveness and
permanence of fingerprints.
3.2.1fingerprint Matching Technique
The minutiae ending and bifurcation are
shown in the Figure 1. A ridge ending is defined as
the ridge point where a ridge ends abruptly. A
bifurcation is defined as the ridge point where a
ridge bifurcates into two ridges. It is accepted that
the minutiae pattern of each finger is unique and
does not change during life period. When human
fingerprint experts determine if two fingerprints are
from the same finger, the matching degree between
two minutiae pattern is one of the most important
factors. The way of human fingerprint experts and
compactness of templates, the minutiae-based
matching method is the most widely studied
matching method. The algorithms which are
compared in this paper belong to the minutiae-
based matching method.
Image-based approaches use the entire
gray scale fingerprint images as a template to
match against input fingerprint images. This
approach needs a large size of storage space and
fingerprint images are illegal to be stored in some
nations.
Minutiae-based approach attempts to get
the similarity degree between two minutiae sets.
However, minutiae-based methods may make the
computation more easyand need to search for the
best correspondence of minutiae pairs or ridge pairs
or use core or delta minutiae point to estimate the
alignment. The problem of lost minutiae or false
minutiae always occurs during the minutiae
detection process. Hence, the corresponding pairs
may not be found under this condition.
Minutiae Based Matching is a technique in which
minutiae are extracted from a fingerprint and stored
as sets of points in a two-dimensional plane and
then minutiae of the fingerprint to be recognized
are extracted and matched with the stored points.
Minutiae matches the alignment between the
template and the input minutiae sets that result in
the maximum number of minutiae pairings.
Automatic fingerprint identification is one of the
most reliable biometric technologies. This is
because of the well-known fingerprint
distinctiveness, persistence, ease of acquisition and
high matching accuracy rates.
3.2.2 Feature Extraction
In this section, we discuss how to extract the
features used by the matching algorithm. We give a
brief description of the ridge and minutiae
extraction.

3.2.2.1ridge And Minutiae Extraction
Given a gray-scale fingerprint image, a
series of steps, computing the orientation image,
frequency image computation, directional
filtering,binarization and thinning, are used to
produce a binaryimage. From the binary image,
minutiae are detected and ridges are extracted by
tracing. Each minutia has four features: x
coordinate, y coordinate, direction and type
(termination or bifurcation). Ridges are represented
as lists of points. To simplify the representation of
ridges, ridges associated with a bifurcation are
treated as three ridges and a closed ridge is broken
at a randomly selected point. Singular points are
extracted using an improved version of the Point
care index method. Each singular point has four
features: x coordinate, y coordinate, direction (only
defined for core), and type (core or delta).
3.2.2.2the Proposed Representation: Minutiae
Direction Map
Phase correlation cannot be used to align
two point sets directly. We present a new
representation called Minutiae Direction Map
(MDM) which is generated by converting minutiae
point sets into a 2D image space. Alignment
parameters are determined using phase correlation
between two MDMs.
Let M=((𝑥1,𝑦1,∝1),…..(𝑥 𝑁, 𝑦 𝑁 ∝ 𝑁)) denote
the set of N minutiae in a fingerprint image. The
image size is CxR and (𝑥𝑖,𝑦𝑖 ∝𝑖) are the three
features (special position and orientation)
associated with the ith minutiae in set M. define the
MDM of set M as 𝜇 𝑀
(m,n),m€[0,C-1], n€[0,R-1].
It contain the angle of minutiae direction at the
position of minutiae points and 0 otherwise, which
is written as
𝜇 𝑀
(m,n)=
𝑐𝑜𝑠 ∝𝑖+ 𝑗 𝑠𝑖𝑛 ∝𝑖, 𝑚 = 𝑥𝑖, 𝑛 = 𝑦𝑖
0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
3.2.2.3 Working Of Minutiae Extraction
The minutiae extractions consist of
series of action. Among all the fingerprint features
minutiae point features with corresponding
orientation maps are unique enough for making
distinctions. The minutiae feature representation
reduces the complex fingerprint recognition
problem to a point pattern matching problem. An
accurate extraction and representation of the
fingerprint feature is very important in automatic
fingerprint recognition systems. Minutiae detection
algorithm need to locate efficiently and accurately
the minutiae points. There are various minutiae
extraction algorithm available, they can be
categorized into four groups. The first type of
groups extracts minutiae points directly from the
gray scale image. A second type of methods
extracts minutiae from binary image. Third type of
methods extracts minutiae using machine learning
methods. The last type of methods extracts
minutiae from binary skeletons. In paper we have
concentrated on binary image.
3.2.2.4 Minutiae Points
Fingerprint ridges are not continuous,
straight ridges. Instead, they are broken, forked,
interrupted or changed directionally. The points at
which ridges end, fork, and change are called
minutia points which provide distinctive,
identifying information.
There are five characteristics of minutia
points in fingerprints:
1. Type
There are several types of minutia points.
The most common are ridge endings and ridge
bifurcations.
Ridge Ending – occurs when a ridge ends abruptly.
Ridge Bifurcation – the point at which a ridge
divides into branches.
Dot or Island – a ridge that is so short it appears as
a dot.
Enclosure – a ridge that divides into two and then
reunites to create an enclosed area of ridge-less
skin.
Short Ridge – an extremely short ridge, but not so
short that it appears as a Dot or an Island.
Core Point-The core point, located at the
approximate center of the finger impression, is used
as a starting reference point for reading and
classifying the print.
2. Orientation
The point on the ridge on which a minutia
resides is called the orientation of the minutia
point.

3. Spatial Frequency
Spatial frequency refers to how far apart
the ridges are in relation to the minutia point.
4. Curvature
The curvature refers to the rate of change
of ridge orientation.
5. Position
The position of the minutia point refers to
its location, either in an absolute sense or relative to
fixed points like the delta and core points
3.3sensor (Scanner Device)
The tool enables developers to perform
basic fingerprint biometric operations: capturing a
fingerprint from a Digital Persona fingerprint
reader, extracting the distinctive features from the
captured fingerprint sample, and storing the
resulting data in a template for later comparison of
a submitted fingerprint with an existing fingerprint
template.
3.3.1 Fingerprint Authentication On A Remote
Computer.
This SDK includes transparent support for
fingerprint authentication through Windows
Terminal Services (including Remote Desktop
Connection) and through a Citrix connection to
Metaframe Presentation Server using a client from
the Citrix Presentation Server Client package.
Through Remote Desktop or a Citrix session, you
can use a local fingerprint reader to log on to, and
use other installed features of, a remote machine
running your fingerprint-enabled application.
The following types of Citrix clients are supported:
�Program Neighborhood
�Program Neighborhood Agent
�Web Client
To take advantage of this feature, your
fingerprint-enabled application must run on
the Terminal Services or Citrix server, not
on the client.
3.3.2developing Citrix-Aware Application
This SDK includes support for fingerprint
authentication through Windows Terminal Services
(including Remote Desktop Connection) and
through a Citrix connection to Metaframe
Presentation Server using a client from the Citrix
Presentation Server Client package.
The following types of Citrix clients are supported
for fingerprint authentication:
�Program Neighborhood
�Program Neighborhood Agent
�Web Client
In order to utilize this support, your
application (or the end-user) will need to copy a
file to the client computer and register it. The name
of the file is DPICACnt.dll, and it is located in the
"MiscCitrix Support" folder in the product
package.
To deploy the DigitalPersona library for Citrix
support:
1. Locate the DPICACnt.dll file in the
"MiscCitrix Support" folder within the
product package.
2. Copy the file to the folder on the client
computer where the Citrix client components
are located (i.e. for the Program Neighborhood
client it might be the "Program
FilesCitrixICA Client" folder).
3. Using the regsvr32.exe program, register the
DPICACnt.dll library.
If you have several Citrix clients installed
on a computer, deploy the DPICACnt.dll library to
the Citrix client folder for each client. If your
application will also be working with Pro
Workstation 4.2.0 and later or Pro Kiosk 4.2.0 and
later, you will need to inform the end-user’s
administrator that they will need to enable two
Group Policy Objects (GPOs), "Use DigitalPersona
Pro Server for authentication" and "Allow
Fingerprint Data Redirection"
3.3.3setting The False Accept Rate
This appendix is for developers who want
to specify a false accept rate (FAR) other than the
default used by the DigitalPersona Fingerprint
Recognition Engine.
3.3.3.1 False Accept Rate(Far)
The false accept rate (FAR), also known
as the security level, is the proportion of fingerprint
verification operations by authorized users that
incorrectly returns a comparison decision of match.
The FAR is typically stated as the ratio of the
expected number of false accept errors divided by
the total number of verification attempts, or the
probability that a biometric system will falsely
accept an unauthorized user. For example, a
probability of 0.001 (or 0.1%) means that out of
1,000 verification operations by authorized users, a
system is expected to return 1 incorrect match
decision. Increasing the probability to, say, 0.0001
(or 0.01%) changes this ratio from 1 in 1,000 to 1
in 10,000.Increasing or decreasing the FAR has the
opposite effect on the false reject rate (FRR), that
is, decreasing the rate of false accepts increases the
rate of false rejects and vice versa. Therefore, a
high security level may beappropriate for an access
system to a secured area, but may not be acceptable
for a system where convenienceor easy access is
more significant than security.

3.3.3.2representation Of Probability
The DigitalPersona Fingerprint
Recognition Engine supports the representation for
the FAR probability that fully conforms to the
BIOAPI 1.1, BIOAPI 2.0, and UPOS standard
specifications. In this representation, the
probability is represented as a positive 32-bit
integer, or zero. (Negative values are reserved for
special uses.). The definition
PROBABILITY_ONE provides a convenient way
of using this representation. PROBABILITY_ONE
has the value 0x7FFFFFFF (where the prefix 0x
denotesbBase 16 notation), which is 2147483647 in
decimalnotation. If the probability (P) is encoded
by the value (INT_N), thenProbability P should
always be in the range from 0 to 1. Some common
representations of probability are listedin column
one of Table 2. The value in the third row
represents the current default value used by
theDigitalPersona Fingerprint Recognition Engine,
which offers a mid-range security level. The value
in the secondrow represents a typical high
FAR/low security level, and the value in the fourth
row represents a typical lowFAR/high security
level.
The resultant value of INT_N is
represented in column two, in decimal notation.
INT_N = P * PROBABILITY_ONE
P=(INT_N)/(PROBABILITY_ONE)
Probability P should always be in the range from 0
to 1. Some common representations of probability
are listed in column one of Table 2. The value in
the third row represents the current default value
used by theDigitalPersona Fingerprint Recognition
Engine, which offers a mid-range security level.
The value in the secondrow represents a high
FAR/low security level, and the value in the fourth
row represents a typical low FAR/high security
level.The resultant value of INT_N is represented
in column two, in decimal notation.
Table 2. Common values of probability and
resultant INT_N values
Probability (P) Value of INT_N in
decimal notation
0.001 = 0.1% = 1/1000 2147483
0.0001 = 0.01% = 1/10000 214748
0.00001 = 0.001% =
1/100000
21475
0.000001 = 0.0001% =
1/1000000
2147
3.4 Database
In our project we are using both local and
centralized database. The data will be fetched from
the local database. But the updation will be taken in
both database. From the fingerprint we are taking
major points like ridges,bifurcation and valleys
from that we are ploting points using a direct
mapping method only the points are stored in the
database in binary format but scanner takes
fingerprint as a template.
IV. CONCULSION
In this paper, we have presented a
fingerprint-based management system. The
developed system is part of a fingerprint
recognition/authentication system based on
minutiae points. The system extract the local
characteristic of a fingerprint which is minutiae
points in template based. Templates are matched
during both registration and verification processes.
For improved quality control during the registration
or verification process, a matching score was used
to determine whether correct user or not .The
matching score was specified so that only sets of
minutiae data that exceed the score will be accepted
and data below the score will be rejected.
Therefore,Fingerprint Recognition using Minutia
Score Matching method was used for matching.
In this system, a framework for electronic voting
machine based on biometric verification is
proposed and implemented. The proposed
framework ensures secured identification and
authentication processes for the voters and
candidates through the use of fingerprint
biometrics. In the project wehave try to reduce the
search time by using the local database instead of
using one centralized database.
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A SECURITY BASED VOTING SYSTEMUSING BIOMETRIC

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