V.online Voting System

8/8/2019 V.online Voting System

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ONLINE VOTING SYSTEM:

India is biggest democracy in the world. In any democratic country, its people

choose their leader for leading the country through voting system. In India, population is

approximately 10 billion. Government of India has set up an Election Commission for

conducting the election all over India. Elections are held at various level i.e. Lok Sabha election

to choose Government at center, assembly election to choose government in state assembly by

the people of state, further more there is municipal council elections and nagarpalika election in

every cities or constituency. So make the election more flexible, independent and secure, election

commission spent huge amount of money. But hardly 50 percent voting occur all over India.

Most of people are out of their voting area due to some reasons such as on election duty, and

some are on border for nation service. Voting percentage is very low due to environmental

conditions or natural calamities. There is long queue for voting at bigger cities, thus people avoid

to vote.

The purposed solution for making voting more flexible, secure and independent is voting

through internet. In the proposed system, a unique_id is generated that is mapped on to finger

print database and setup secure networkbetween voting booths and election commission. This

network required high security and thus online voting will be enabling only on and only for the

Election Day.

UNIQUE_ID: In this proposed system, a unique number will allocated to every citizen of India,

this number will be generated using constitutions division principle means a person will be

allotted a unique id according to related constituency in which voter is residing. This unique id

consists of state code (2 Bytes), constitutes area code (4 Bytes), ward code (4 bytes) and person-

code (6 Bytes). This unique id is stored in database corresponding to the voters information.

Information should also include four finger impressions. The related information of any person

can be extracted by using this unique_id plus finger impression. The finger impression work as

authentication provided to voter and use as signature.

State code(2 bytes) Constituency code(4 Ward code(4 byte) Person code(6 byte)


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bytes)

DATABASE: In this system, database will require high security. Database contain very sensitive

information, thus it should be maintain by trusted parties. Thus all the transactions should be in

encrypted form. Database can be maintained by Election Commission. It requires high security

so that no intruder can crack the database.

NETWORK: Network plays an important role in this solution. As voting is very sensitive

matter for any country, it require high alert and high security at voting area. At internet level,

network required high security, speed and uninterrupted connection.

SERVER: The main purpose of server is to provide the authorization to voters and admin

personnel and, manages and updating voting system. Server is responsible for handing the

candidate list, providing authorization to voters and enabling and disabling the voting portal.

RESULT: Result will be enabled only on the result day. DC of that constituency can

operate/declare result on specified date and time. This solution handle worlds biggest database


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for retrieval huge information from huge database. This solution can be used for other public

application such as police enquire and criminal identification.

FINGERPRINT ANALYZER: Biometrics is the science and technology of measuring

and analyzing biological data. In information technology, biometrics refers to technologies that

measure and analyze human body characteristics, such as fingerprints, eye retinas and irises,

voice patterns, facial patterns and hand measurements, for authentication purposes.

The main requirement in this system is that the voter should have internet

facility and his/her system should have a fingerprint bio-metric device. This device analyses the

fingerprint and send the information to election commission. Further, Election existing

commission matches the fingerprint with their database. If fingerprint is matched with database,

the server authenticate the voter to vote otherwise, it neglect the voter. The fingerprint analyzer

required high software and hardware security to avoid fishing, intruding and fake voting.

LITERATURE SURVEY

What was wrong with cards and PINs?

PINs (personal identification numbers) were one of the first identifiers to offer

automated recognition. However, it should be understood that this means recognition of the PIN,

not necessarily recognition of the person who has provided it. The same applies with cards and

other tokens. We may easily recognize the token, but it could be presented by anybody. Using

the two together provides a slightly higher confidence level, but this is still easily compromised

if one is determined to do so.

A biometrics however cannot be easily transferred between individuals (replacement part

surgery is outside the scope of this paper) and represents as unique an identifier as we are likely

to see. If we can automate the verification procedure in a user-friendly manner, there is

considerable scope for integrating biometrics into a variety of processes.

The keys are usually stored in a secure location (e.g., tamper-resistant hardware) and

password-based authentication is commonly used for controlling access to cryptographic keys.

However, passwords can be easily lost, stolen, forgotten or guessed using social engineering and

dictionary attacks. Limitations of password-based authentication can be alleviated by using


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stronger authentication schemes such as biometrics. Biometric systems establish the identity of a

person based on his/her anatomical or behavioral traits such as face, fingerprint, iris, voice, etc.

Biometric authentication is more reliable than password-based authentication because biometric

traits cannot be lost or forgotten and it is difficult to share or forge these traits.

cards and PINs The early applications of Biometric technology were limited to the area of

forensics. These original applications of fingerprint, which relied on images from inked ten-print

cards that were captured by digital cameras, increased not only the speed of the identification

response, but also the level of accuracy. The criminal and civil systems differ in terms of their

complexity and cost because of their differing purposes. Additionally, while the search databases

for forensic applications are maintained for law enforcement purposes, the databases for civil

applications are operated and maintained by non-law enforcement personnel. The requirements

for record retention, confidentiality, and even accuracy can be very different for civil

applications. The use of fingerprint technology in civil applications, particularly public benefits

programs, has not been without criticism.

Techniques:

The goal of the new generation of fingerprint technique is to support the matching with

Level 3 features, increasing the system security to the governmental and Police levels. Here we

will discuss three template selection criteria, being minutiae-based, and correlation-based, and

coherence based.


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1. Minutiae Acquisition Technique

Most of the finger-scan technologies are based on Minutiae. Minutia-based techniques represent

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.

This work also concentrates on same approach.

A drawback of this technique is that it suffers from most of the problems of minutiae-

based systems. Still, many false minutiae are extracted, causing at least a part of the templates to

be rather unreliable.

(OR)

II. Minutiae-Based ApproachMost _ngerprint veri_cation systems follow a minutiaebased

approach, see e.g. [1]. Minutiae-based _ngerprintveri_cation systems _rst extract the minutiae, shown inFigure 3, from the _ngerprint images. Then, the decisionis based on the correspondence of the two sets of minutiaelocations.Minutiae-based _ngerprint veri_cation systems use alarge number of successive processing steps. In general,the following steps can be identi_ed in a minutiae-basedsystem:

_ directional _eld estimation,_ adaptive _ltering for noise reduction

_ thresholding to obtain a binary _ngerprint image,_ morphological operations like thinning to obtain ridgesthat are only one pixel wide,

_ minutiae extraction from the thinned image,_ application of heuristics to reduce the number of falseminutiae,

_ registration of minutiae templates by Hough transform,_ matching score computation.The main drawback of the minutiae-based approach isthe error propagation from the minutiae extraction to thedecision stage. In general, the extracted minutiae templatescontain a number of false minutiae, while also someminutiae will be missed. This is especially the case whenusing bad-quality _ngerprints. The heuristics do not catchall spurious minutiae, while they might reject some of the

genuine minutiae. As a result, the decision stage has tocompare two a_ected sets.

2. Correlation-Based Template Selection:

The second method satisfies the template requirements most directly. In this method, templates

are selected by checking how well they fit at other locations in the same fingerprint. If a template


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fits almost as well at another location as it does at its original location, it is not a useful template.

However, if a template fits much worse at all other locations in the fingerprint, it is a template

that offers a lot of distinction. Therefore, the ratio of fit at a template's original location to the fit

at the next best location can be used as a template selection criterion. Since the correlation-based

checking is carried out by means of template matching, this method consumes a lot of

computational power. This makes it a less attractive method to use. However, it is for instance

possible to combine this approach with the previous two methods. In that case, possible template

locations are extracted by one of the methods of the previous subsections. Then, the correlation

characteristics of those locations are checked as an additional selection criterion.

.

(OR)III. Correlation-Based Fingerprint MatchingIn order to deal with some of the problems of theminutiae-based approach, we have chosen an alternativeapproach. Instead of only using the minutiae locations, ourmethod directly uses the gray-level information from the

_ngerprint image, since a gray-level _ngerprint image contains much richer, more discriminatory,information thanonly the minutiae locations. Those locations only characterizea small part of the local ridge-valley structures [2],[3], [4].The correlation-based _ngerprint veri_cation system isinspired by [5]. It _rst selects characteristic templates in

the primary _ngerprint. Then, template matching is usedto _nd the positions in the secondary _ngerprint at whichthe templates match best. Finally, the template positionsin both _ngerprints are compared in order to make thedecision whether the prints match

A. Template SelectionThe _rst step in the template matching algorithm is theselection of appropriate templates. This is a crucial step,since good templates will be easily localized in the secondaryprint at the right position, while bad templates willnot. More generally, the templates should be uniquely localized

in the secondary _ngerprint. The template should_t as well as possible at the same location, but as badly aspossible at other locations.The _rst template property to consider is the size of thetemplates. There must be an optimal template size, as canbe seen from two extreme situations. When the entire _ngerprintis taken as template, any attempt to align speci_ccorresponding positions will lead to misalignments at otherpositions due to shape distortions. On the other hand, iftemplates of only 1 by 1 pixel are chosen, it is clear that


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the templates do not o_er enough distinction. Experimentshave shown that a template size of 24 by 24 pixels is a goodcompromise.The second problem in selecting the right templates iswhich template positions to chose. Research has shown forinstance, that a template that contains only parallel ridgevalleystructures cannot be located very accurately in thesecondary _ngerprint. In this paper, three template selectioncriteria are proposed, being minutiae-based, coherencebasedand correlation-based.

A.1 Minutiae-Based Template SelectionAs mentioned before, templates that only contain parallelridge-valley structures do not o_er much distinction.On the other hand, when a template contains one or moreminutiae, it will be much easier to _nd the correct locationin the secondary print. Using this assumption, one possibleapproach to select template locations is to extract minutiaefrom the _ngerprint image and to de_ne templates aroundthe minutiae locations.

A drawback of this technique is that it su_ers from most

of the problems of minutiae-based systems. Still, manyfalse minutiae are extracted, causing at least a part of thetemplates to be rather unreliable.

A.2 Coherence-Based Template SelectionThe coherence of an image area is a measure that indicateshow well the local gradients are pointing in thesame direction. In areas where the ridge-valley structures are only parallel lines, the coherence is very

high, while in

noisy areas, the coherence is low [6], [7].Templates that are chosen in regions of high coherence

values cannot be located reliably in a second _ngerprint[8]. However, at locations around minutiae, more grayscale

gradient orientations are present, resulting in a signi_cantly lower coherence. Therefore, the coherence can beused as an appropriate measure that indicates the presenceof minutiae as well as a measure that indicates how well atemplate can be located in the secondary _ngerprint.

At _rst sight, this template selection criterion seems toconflict with segmentation [6]. While segmentation choosesthe regions of low coherence values as noise or backgroundareas, now the regions that have low coherence values haveto be chosen as reliable templates. However, this contradictionis solved by the notion of scale [9]. Segmentationselects a large, closed area as foreground, in which holes andother irregularities are _lled by means of morphology. Instead,

the coherence based template selection only searchesfor local coherence dips in this foreground area.The drawback of this method is that noisy areas showcoherence dips as well, while these are certainly not reliabletemplates. This problem may be solved by using appropriate

_flters.


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A.3 Correlation-Based Template Selection

The third method satis_es the template requirementsmost directly. In this method, templates are selected bychecking how well they _t at other locations in the same

_ngerprint. If a template _ts almost as well at anotherlocation as it does at its original location, it is not a useful

template. However, if a template _ts much worse atall other locations in the _ngerprint, it is a template thato_ers a lot of distinction. Therefore, the ratio of _t ata template's original location to the _t at the next bestlocation can be used as a template selection criterion.Since the correlation-based checking is carried out bymeans of template matching, this method consumes a lotof computational power. This makes it a less attractivemethod to use. However, it is for instance possible to combinethis approach with the previous two methods. In thatcase, possible template locations are extracted by one ofthe methods of the previous subsections. Then, the correlationcharacteristics of those locations are checked as an

additional selection criterion

B. Template MatchingOnce the templates have been selected in the primary

_ngerprint, their corresponding positions in the secondary_ngerprint have to be found. This can be done using standardtemplate matching techniques.The template is shifted pixelwise over the secondaryprint. At each position, the gray-level distance betweenthe template and the corresponding area in the secondaryprint is determined by summing the squared gray-level differencesfor each pixel in the template. After having shiftedthe template over the entire _nger, the location where the

distance is minimal is chosen as the corresponding positionof the template in the second _ngerprint.

3. Coherence-Based Template Selection

The coherence of an image area is a measure that indicates how well the local gradients are

pointing in the same direction. In areas where the ridge-valley structures are only parallel lines,

the coherence is very high, while in noisy areas, the coherence is low. Templates that are chosen

in regions of high coherence values cannot be located reliably in a second fingerprint. However,

at locations around minutiae, more grayscale gradient orientations are present, resulting in a

significantly lower coherence. Therefore, the coherence can be used as an appropriate measure

that indicates the presence of minutiae as well as a measure that indicates how well a template

can be located in the secondary fingerprint.


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Correlation-based techniques are a promising approach to fingerprint matching for this

new generation of fingerprint sensors. Correlation uses the gray level information of the

fingerprint image and can take into account all dimensional attributes of a fingerprint, providing

enough image resolution. As a matter of fact, these methods have been used successfully for

fingerprint matching with conventional sensors, as demonstrated in recent Fingerprint

Verification Competitions (FVC). The major drawback of correlation-based techniques is the

high computational effort required.

Approaches to correlation-based fingerprint matching have already been proposed.

Among the most important aspects of these techniques are the selections of appropriate areas of

the fingerprint image for correlation and the computational effort required to consider translation

and rotation between the fingerprint images. In order to account for displacement and rotation,

Ouyang et al. propose the use of a local Fourier-Mellin Descriptor (FMD). However, since the

center of relative rotation between two compared fingerprints is unknown, the local FMD has to

be extracted for a large number of center locations. Other works correlate ridge feature maps to

align and match fingerprint images, but do not consider rotation yet.

This thesis project presents a correlation-based fingerprint matching algorithm that is also

suitable for matching Level 3 features. In this algorithm, the matching is focused on high quality

and distinctive fingerprint image regions in order to minimize the correlation effort and to avoid

noisy or non relevant areas. The selection of candidate regions for correlation-based matching is

based on image quality measurements. For low resolution fingerprint images, the coherence of

the orientation field is used. This measurement is based in the behavior of a low level feature.

For high resolution fingerprint images, we propose the use of the standard deviation of the

wavelet coefficients of the fingerprint image. This measurement takes advantage of the

capability of the wavelet transform to select relevant image information considering at the same

time spatial and frequency domains. This region selection criterion exploits the possibilities of

the information confined inside the high resolution fingerprints.

It first selects characteristic templates in the primary fingerprint. Then, template matching

is used to find the positions in the secondary fingerprint at which the templates match best.

Finally, the template positions in both fingerprints are compared in order to make the decision

whether the prints match.


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Algorithm for correlation matching technique:

ACQUISTION

MATCHING/UNMATCHING

DECISION

PREPROCESSING

COMMON REGION EXTRACTION

CORRELATION EXECUTION FOR

MATCHING


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1. ACQUISITION AND PREPROCESSING

A. ACQUISITION

The first step in the fingerprint matching algorithm is the selection of appropriate temletes. This

is a crucial step, since good templates will be easily localized in the secondary print at the right

position, while bad templates will not. More generally, the templates should be uniquely

localized in the secondary fingerprint. The template should fit as well as possible at the same

location, but as badly as possible at other locations. The first template property to consider is the

size of the templates. There must be an optimal template size, as can be seen from two extreme

situations. When the entire fingerprint is taken as template, any attempt to align specific

corresponding positions will lead to misalignments at other positions due to shape distortions. On

the other hand, if templates of only 1 by 1 pixel are chosen, it is clear that the templates do not

offer enough distinction. Experiments have shown that a template size of 24 by 24 pixels is a

good compromise. The problem of template selection with regard to fingerprints may be posed

as follows: Given a set of N fingerprint images corresponding to a single finger, select K

templates that best represent the variability as well as the typicality observed in the N images,

KN. Currently; we assume that the value of K is predetermined. This systematic selection of

templates is expected to result in a better performance of a fingerprint matching system

compared to a random selection of K templates out of the N images.The second problem in selecting the right templates is which template positions to chose.

Research has shown for instance, that a template that contains only parallel ridge valley

structures cannot be located very accurately in the secondary fingerprint.


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Comparison of same region of same fingerprint acquired by webcam and dedicated

sensor.

B. PREPROCESSING:

Correlation-based matching techniques use the fingerprint image directly without any extraction

step, so the quality of the fingerprint must be good enough for the CC computation. This implies

a moderate preprocessing effort. The preprocessing used for the proposed algorithm whose main

steps are: normalization, low frequency noise filtering, orientation field estimation and frequency

estimation with their respective coherences, Gabor filtering and finally equalization.

(OR)

Correlation-based matching techniques use the fingerprintimage directly without any extraction step, so the quality ofthe fingerprint must be good enough for the CC computation.This implies a moderate preprocessing effort.The preprocessing used for the proposed algorithm is

based in [9], whose main steps are: normalization, lowfrequency noise filtering, orientation field estimation andfrequency estimation with their respective coherences, Gaborfiltering and finally equalization. Special relevance has theorientation Field estimation because it will be used later on bythe following steps.


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2. COMMON REGION OF INTERSET.

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 bounded region is confusing with those spurious minutias that are generated when

the ridges are out of sensor or webcam. To extract the ROI, a two method is used. The first step

is block direction estimation and direction variety check, while the second is intrigued from

some morphological methods. Two morphological operations called OPEN and CLOSE are

adopted. The OPEN operation can expand images and remove peaks introduced by background

noise. The CLOSE operation can shrink image and eliminate small cavities.

(OR)

Afteralignment, both fingerprints are analyzed in ordertodetermine candidate regions for correlation. Selection of localregions for correlation is required, since using the entirefingerprint will be computationally very expensive and willcorrelate badly due to fingerprint deformation and noise. Onthe other hand, the local regions should be highly distinctive.Several approaches for selecting local regions are discussedin [5].A typical way to choose region candidates consists incomputing auto-correlation of the image in order to determinethe more distinguishable parts of the image. However, this

approach requires a huge computational effort. Regions

around core orregions where ridges have high curvaturemay be selected as candidates, but correlation results maybe bad because these are typically very noisy areas.Besides, core does not appearin all fingerprints.

MINUTIAE EXTRACTION:

After ridges are extracted from input image by applying two morphological operations that

adaptively capture the maximum gray level values along the direction perpendicular to the ridge

orientation. Several heuristics are then applied to remove the holes and speckles in the binary

ridge map. The extracted ridges are then thinned and minutiae are detected in the thinned image.

The location, orientation and the points on the ridge associated with the minutia are stored for

each minutia point. The ridge pints are useful in the alignment of the template and the query

during the minutiae matching stage.

(OR)


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Minutiae extraction is one of the critical steps in fingerprint

verification algorithms. Any missingminutiae or spurious

minutiae introduced at this stage can degrade the performance

of the matching algorithm. Existing structural approaches

forminutiae filtering use heuristics and adhoc rulesto eliminate such false positives, where as gray level approachesare based on using raw pixel values and a supervisedclassifier such as neural networks. We propose two

new techniques forminutiae verification based on non-trivial

gray level features. The proposed features intuitively represents

the structual properties of the minutiae neighborhood

leading to better classification. We use directionally selective

steerable wedge filters to differentiate between minutiae

and non-minutiae neighborhoods with reasonable accuracy.

We also propose a second technique based on Gabor expansion

that results in even better discrimination. We present anobjective evaluation of both the algorithms. Apart fromminutiae

verification, the feature description can also be used forminutiae detection andminutiae quality assessment.

3. CORRELATION EXECUTION FOR MATCHING:

In the literature a number of correlation based algorithms may be found. Rusyn et al.(2002) used

the spectral information obtained from the fingerprint images in correlation, while Hatano et

al.(2002), uses differential correlation computed as the difference between the maximum and

minimum correlations. And Bazen and Grez(2000), takes certain distinctive parts of the template

image and search for them on the query image. However this approach requires template

selection process for each fingerprint image increasing the time required for the process.

Our algorithm for fingerprint matching is shown in figure 1. The minutiae extraction

algorithm is applied to the template and query fingerprint images and the minutiae points and the

associated ridges points are extracted. The template and query image are enhanced using a

modified version of the Gabor filter-based enhancement technique purposed by Hong. The

enhanced image renders the fingerprint ridge structure in the form of smooth gray scale ripples

with little residual noise. Finally the binary ridge map image is obtained by locally adaptive

thresholding technique.


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4. MATCHING PROCEDURE:

1. Start

2. In the preprocessing stage, register the input or testing image g so as to make it invariant from

rotation, and scale. The resultant image is g.

3. Extract the common region f and g from both the images f and g.

4. Compute the cross-correlation coefficients between the common regions extracted from

template fingerprint image f and that of testing fingerprint image g.

5. Record the highest correlation coefficient value as the result.

6. End

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V.online Voting System