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  • 1. INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011 A Novel design of Electronic Voting System Using Fingerprint D. Ashok Kumar#1, T. Ummal Sariba Begum#2 #1 #2 Department of Computer Science, V .S.S. UGC Research Fellow, V.S.S. Government Arts Government Arts College, College, Pulankurichi – 630 405, Sivagangai, Tamil Nadu, Pulankurichi – 630 405, Sivagangai, Tamil Nadu, India IndiaAbstract— The heart of democracy is voting. The heart of For many years, paper-based ballot is used as a wayvoting is trust that each vote is recorded and tallied with to vote during campus election day. This matter putaccuracy and impartiality. The accuracy and impartiality are an inefficient way of voting process as students havetallied in high rate with biometric system. Among these to queue up to register their name before they canbiometric signs, fingerprint has been researched thelongest period of time, and shows the most promising vote. Furthermore, the traditional way of voting willfuture in real-world applications. Because of their take a long process and time. So, the novel electronicuniqueness and consistency over time, fingerprints have voting using minutiae will become the best solutionbeen used for identification over time. However, because of for the matters; besides provide easier way of voting.the complex distortions among the different impression of Compared to existing voting system the Electronicthe same finger in real life, fingerprint recognition is still a voting has several advantages like: Electronic votingchallenging problem. Hence in this study, the authors are system is capable of saving considerable printinginterested in designing and analysing the Electronic Voting stationery and transport of large volumes of electoralSystem based on the fingerprint minutiae which is the core material. It is easy to transport, store, and maintain. Itin current modern approach for fingerprint analysis. Thenew design is analysed by conducting pilot election among completely rules out the chance of invalid votes. Itsa class of students for selecting their representative. use results in reduction of polling time, resulting inVarious analysis predicted shows that the proposed fewer problems in electoral preparations, law andelectronic voting system resolves many issues of the order, candidate’s expenditure, etc. and easy andcurrent system with the help of biometric technology. accurate counting without mischief at the countingKeywords— Biometric, Fingerprint, Minutiae, centre. It is also eco friendly [8].Electronic Voting. Biometrics is the automated recognition of individuals based on their behavioural and biological I. INTRODUCTION characteristics. Biometric recognition means by measuring an individuals suitable behavioural and biological characteristics in a recognition inquiry and Elections allow the populace to choose their comparing these data with the biometric referencerepresentatives and express their preferences for data which had been stored during a learninghow they will be governed. Naturally, the integrity of procedure, the identity of a specific user isthe election process is fundamental to the integrity of determined. A fingerprint is an impression of thedemocracy itself. The election system must be friction ridges, from the surface of a fingertip.sufficiently robust to withstand a variety of fraudulent Fingerprints have been used for personalbehaviours and must be sufficiently transparent and identification for many decades, more recentlycomprehensible that voters and candidates can becoming recognition is nowadays one of the mostaccept the results of an election. In context of important and popular biometric technologies mainlyWestern democracies current crisis, electronic voting because of the inherent ease in acquisition thehas become a very popular topic of discussion in numerous sources (ten fingers) available foracademic and technical circles. collection, and the established use and collections byVoting is a method for a group such as a meeting or law enforcement agencies. Automatic fingerprintan electorate to make a decision or express an identification is one of the most reliable biometricopinion—often following discussions, debates, or technologies. This is because of the well knownelection campaigns. It is often found in democracies fingerprint distinctiveness, persistence, ease ofand republics. Electronic voting (also known as e- acquisition and high matching accuracy is a term encompassing several different Fingerprints are unique to each individual and theytypes of voting, embracing both electronic means of do not change over time. Even identical twins do notcasting a vote and electronic means of counting carry identical fingerprints. Scientific research invotes. 12
  • 2. INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011areas such as biology, embryology, anatomy and of students to chose their representative and Sectionhistology has supported these findings [28]. V concludes and states the future work plans. Because biometric identifiers cannot be easilymisplaced, forged, or shared, they are considered II. ISSUES OF PRESENT VOTING SYSTEMmore reliable for person recognition than traditional There has been several studies on usingtoken or knowledge based methods. The objectives computer technologies to improve elections [5, 38,of biometric recognition are user convenience (e.g., 21, 22, and 29].money withdrawal without ATM card or PIN), better These studies caution against the risks of movingsecurity (e.g., difficult to forge access), and higher too quickly to adopt electronic voting system,efficiency (e.g., lower overhead for computer because of the software engineering challenges,password maintenance). The tremendous success of insider threats, network vulnerabilities, and thefingerprint based recognition technology in law challenges of auditing.enforcement applications, decreasing cost of Researchers in the electronic voting field havefingerprint sensing devices, increasing availability of already reached a consensus pack of following coreinexpensive computing power, and growing identity properties that an electronic voting system shouldfraud/theft have all ushered in an era of fingerprint- have [30]:based person recognition applications in commercial, Accuracy: (1) it is not possible for a vote to becivilian, and financial domains. So the Electronic altered, (2) it is not possible for a validated vote to bevoting system has to be improved based on the eliminated from the final tally, and (3) it is notcurrent technologies viz., biometric system. possible for an invalid vote to be counted in the final There are some previous works which uses tally.fingerprint for the purpose of voter identification or Democracy: (1) it permits only eligible voters to voteauthentication. As the fingerprint of every individual is and, (2) it ensures that eligible voters vote only once.unique, it helps in maximizing the accuracy. A Privacy: (1) neither authorities nor anyone else candatabase is created containing the fingerprint of all link any ballot to the voter who cast it and (2) no voterthe voters in the constituency. Illegal votes and can prove that he voted in a particular way.repetition of votes is checked for in this system. Verifiability: anyone can independently verify that allHence if this system is employed the elections would votes have been counted fair and free from rigging. Collusion Resistance: no electoral entity (any server Fingerprint recognition or fingerprint authentication participating in the election) or group of entities,refers to the automated method of verifying a match running the election can work in a conspiracy tobetween two human fingerprints. Fingerprints are introduce votes or to prevent voters from voting. If allone of many forms of biometrics used to identify an entities conspire this property isn’t achieved. So, thisindividual and verify their identity. Extensive research characteristic should be measured in terms of thehas been done on fingerprints in humans. Two of the total number of entities that must conspire tofundamentally important conclusions that have risen guarantee a successful interference in the election.from research are: (i) a persons fingerprint will not Availability: (1) the system works properly as long asnaturally change structure after about one year after the poll stands and (2) any voter can have access tobirth and (ii) the fingerprints of individuals are unique. it from the beginning to the end of the poll.Even the fingerprints in twins are not the same. In Resume Ability: the system allows any voter who hadpractice two humans with the same fingerprint have interrupted his/her voting process to resume it ornever been found [7]. In this study, for the fingerprint restart it while the poll standsauthentication the minutiae based matching is The existing elections were done in traditional way,considered for higher recognition accuracy. Also, the using ballot, ink and tallying the votes afterward. Butmatching accuracy of fingerprint based authentication this system prevents the election from beingsystems has been shown to be very high. Fingerprint accurate. Problems encounter the usual elections are– based authentication systems continue to dominate as follows:the biometrics market by accounting for almost 52% • It requires human participation, in tallying theof authentication systems based on biometric traits votes that makes the elections time consuming[2]. and prone to human error. This paper is organized as follows: The section IIdescribes the issues of the present voting system, • The voter find the event boring resulting to asection III discusses the fundamentals of finger print small number of voters.authentication system Section III describes theproposed novel application for Electronic Voting • Deceitful election mechanism.Systems, Section IV describes the ExperimentalResults of a pilot election conducted among a class 13
  • 3. INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011 • Constant spending funds for the elections staff every year. So, the proposed electronic voting system has tobe addressed these problems. III FUNDAMENTALS OF FINGERPRINT AUTHENTICATION SYSTEM The types of information that can be collected froma fingerprints friction ridge impression can becategorized as Level 1, Level 2, or Level 3 features Fig 2 Characteristic Attributes of a Minutiaeas shown in fig 1. Level 2 features or minutiae referto the various ways that the ridges can bediscontinuous. These are essentially Galton In a recently published World Biometric Marketcharacteristics, namely ridge endings and ridge Outlook (2005-2008), analysts predict that thebifurcations. A ridge ending is defined as the ridge average annual growth rate of the global biometricpoint where a ridge ends abruptly. A bifurcation is market is more than 28%, by 2007 [11]. Thedefined as the ridge point where a ridge bifurcates technologies that would be included in this areinto two ridges. Minutiae are the most prominent fingerprint technology by 60%, facial & iris by 13%,features, generally stable and robust to fingerprint keystroke by 0.5% and digital signature scans byimpression conditions. The distribution of minutiae in 2.5% Basically there are two types of fingerprinta fingerprint is considered unique and most of the Recognition System:automated matchers use this property to uniquely (1) AFAS ( Automatic Fingerprintidentify fingerprints. Uniqueness of fingerprint based Authentication System)on minutiae points has been quantified by Galton [7]. (2) AFIS ( Automatic FingerprintStatistical analysis has shown that Level 2 features Identification / Verification System )have sufficient discriminating power to establish theindividuality of fingerprints [34]. 1) AFAS (Automatic Fingerprint Authentication System) Components of AFIS are: [40] [10][42] 1. Physical Fingerprint required as input. 2. Input is processed by using various image processing tools and databases and Classification of Fingerprints. The basic fundamental steps of these systems (see Fig (3) are image acquisition, pre- processing segmentation, enhancement etc), feature extraction, matching along with classification through databases. Authentication or verification systems authenticate the persons identity by comparing the own biometric template(s) stored in database (One- to-One comparison). An identification system recognize an individual by searching the entire templates in database for match (One-to-ManyFig 1 Fingerprint features at Level 1, Level 2, and Level 3 [36, 23] Comparison) [31] [17]. The Fig 2 shows the clear view of minutiae. Aminutia is characterized by its location andorientation. Fig 3 Typical Structure for Fingerprint System [16] 14
  • 4. INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011 2) AFIS (Automatic Fingerprint Identification/ significant percentage of fingerprint images is of poorVerification System) quality. In fact, a single fingerprint image may contain A fingerprint recognition system operates either in regions of good, medium, and poor quality. Thus anverification mode or in identification mode. The enhancement algorithm which can improve thevarious stages in a fingerprint verification system are quality of ridge structure is necessary. A survey onshown in Fig 4. different enhancement techniques can be found in [2]. The paper [9] describes the popular enhancement algorithm by Sharat et al. [33], which used contextual filtering in Fourier domain. In paper [24], the enhancement technique like histogram, Fourier and Gabor are compared and best technique gabor is found. There has been lot of interesting work done in enhancing fingerprints. Sherlock [35] proposed enhancing the features in a fingerprint image by directional Fourier filtering. This frequency domain filtering is computationally less expensive than the spatial convolution of the image with filters. The filtered image is usually binarized or thinned for Fig 4 Architecture of Fingerprint Verification System feature extraction. But there has been an effort to The first stage is the data acquisition stage in extract features from grey scale images, Maio andwhich a fingerprint image is obtained from an Maltoni [16] proposed an algorithm to extract featuresindividual by using a sensor. The next stage is the from gray scale images. The feature extractionpre-processing stage in which the input fingerprint is algorithm has usually been employed on thinnedprocessed with some standard image processing images. Jain [11] and Ratha [27] developedalgorithms for noise removal and smoothening. The algorithms for thinned images, their approach haspre-processed fingerprint image is then enhanced involved local neighbourhood based processing onusing specifically designed enhancement algorithms the images.which exploit the periodic and directional nature of Many authors have identified the need to performthe ridges. The enhanced image is then used to post processing on fingerprint images to remove theextract salient features in the feature extraction false minutiae, Ratha et al., where the minutiae arestage. Finally, the extracted features are used for validated based upon heuristics like distance. Sincematching in the matching stage. the fingerprint based system rely on matchingData Acquisition: Traditionally, in law enforcement between the query fingerprint and the databaseapplications fingerprints were acquired off-line by fingerprint, classification of the database results intransferring the inked impression on a paper. the query only searching in a particular class. ManyNowadays, the automated fingerprint verification attempts [16] [20] have been made to classify thesystems use live-scan digital images of fingerprints fingerprints based upon core as well as delta points;acquired from a fingerprint sensor. These sensors these have been point based approach. Theare based on optical, capacitance, ultrasonic, thermal matching forms the heart of any fingerprint; the queryand other imaging technologies. The techniques fingerprint of even a client is usually a transformedfollowed in these sensors are discussed in [2]. version of the database fingerprint. This involvedImage Pre-processing: The preprocessing steps try registration of the images before obtaining the compensate for the variation in lighting, contrast There have been several prior approaches thatand other inconsistencies which are introduced by addressed this. Ranade and Rosenfield [26]the sensor during the acquisition process. The paper proposed an iterative approach for obtaining point[1] discusses the pre- processing steps generally correspondences.used, which are Gaussian Blur, Sliding-window The fingerprint enhancement techniques proposedContrast Adjustment, Histogram based Intensity by Chen et al. [27], is based on the convolution of theLevel and etc., image with Gabor filters which has the local ridgeFingerprint Image Enhancement: The Performance orientation and ridge frequency. The algorithmof fingerprint feature extraction and matching includes normalization, ridge orientation estimation,algorithms relies heavily on the quality of the input ridge frequency estimation and filtering.fingerprint images. Due to various factors such as The paper [24] evaluates the performance of threeskin conditions (e.g., we, dry, cuts, scars and types of image enhancement techniques and theirbruises). Non-uniform finger pressure, noise impact in minutiae detection. In this work we haveintroduced by sensor and inherently poor-quality taken the account of hough transformation andfingers (e.g., manual workers, elderly people), a 15
  • 5. INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011analyzed the results with previous referred to the core point and finger code obtained. The papertransformations. [32] proposed a fingerprint matching which is more robust at shift and rotation of the fingerprints while itFeature Extraction: In this section I describe is of high accuracy. A Survey on Ridge feature basedvarious levels of feature in fingerprint. The levels of matching techniques is proposed in paper [1].features which is to be extracted are Minutiae, Pores, Minutiae based MatchingRidge Contour Extraction. Let T and Q be the feature vectors, representingMinutiae Extraction The next step after enhancement minutiae points, form the template and queryof the image is the extraction of minutiae. The fingerprint, respectively. Each element of theseenhanced image is binarized first in this step. The feature vectors is a minutiae point, which may beskeleton of the image is then formed. The minutiae described by different attributes such as location,points are then extracted by the following method. orientation, type, quality of the neighbourhood region,The binary image is thinned as a result of which a etc. The most common representation of a minutiaeridge is only one pixel wide. The minutiae points are is the triplet x, y, θ where x, y is the minutiae locationthus those which have a pixel value of one (ridge and θ is the minutiae angle. Let the number ofending) as their neighbour or more than two ones minutiae in T and Q be m and n, respectively.(ridge bifurcations) in their neighbourhood. This ends T=m1, m2,…, mm, mi = xi, yi, θi, i =1…mthe process of extraction of minutiae points. Let (x, y) denote a pixel on a thinned ridge, and Q=m’1, m’2,…,m’ni m’j = x’j, y’j, θ ‘j, j=1…nN0, N1,…, N7 denote its eight neighbours. A pixel (x, (9)y) is a 7 Ridge ending if ( ∑ Ni ) = 1 A minutiae mi in T and mj’ in Q are considered i =0 matching, if following conditions are satisfied: 7 Ridge bifurcation if ( ∑ Ni ) > 2 i=0Pores extraction: Pores are extremely fine details 2 2which are lost after the enhancement stage. sd ( m’j , mi)= ((x’j-xi) +(y’j-yi) ≤ roKryszczuk et al. [15] and [3] have proposedskeletonization based approach for pore extraction. dd (m’j , mi) = min ( |θ’j – θi|, 360 - |θ’j – θi|) ≤ θoJain et al. [13] have proposed a pore extraction (10)technique directly from gray scale image. . A recentstudy [9] by the International Biometric Group hasproposed a new approach for pore extraction whichutilizes orientation information of pores along with the Here, r0 and θ0 are the parameters of the tolerancelocation information. window which is required to compensate for errors inRidge Contour Extraction: Ridge contours can be feature extraction and distortions caused due to skinextracted by using classical edge detection plasticity.algorithms. Jain et al [13] have proposed analgorithm to extract the ridge contours which used a The number of “matching” minutiae points can besimple filter to detect ridge contours. maximized, if a proper alignment (registration parameters) between query and template fingerprintsFingerprint Matching: A variety of automatic can be found. Correctly aligning two fingerprintsfingerprint matching algorithms have been proposedin the pattern recognition literature. A useful literature requires finding a complex geometricalsurvey on fingerprint recognition can be found in [2]. transformation function (map ()), that maps the two One family uses correlation based matching [4]. [6] minutiae set (Q and T) the desirable characteristics ofand [19]. Correlation matching is less tolerant to map () functions are: it should be tolerant distortion; itrotational and translational variances of the should recover rotation, translation and scalefingerprint and of extra noise in the image. Another parameters uses Minutiae-based matching [37], [10], [14].Minutiae matching are certainly the most well known For the fingerprint enhancement technique, weand widely used method for fingerprint matching. In compare the four types of fingerprint enhancementgeneral minutiae matching are considered by most to technique viz., Histogram, Fourier filter, Houghhave, higher recognition accuracy. The last family Transform and Gabor filter and find the bestuses Ridge feature based matching [12]. Jain et al enhancement based on the following measures[12] proposed a local texture analysis where thefingerprint area of interest is tessellated with respect 16
  • 6. INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011 III. A NOVEL ELECTRONIC VOTING SYSTEM allowed to vote. Otherwise he is rejected and give the The main core of this study is to design an beep sound. The person who is authenticated mayelectronic voting system based on fingerprint vote for their beloved one by giving his fingerprint tominutiae is discussed in this section by two phases: i) the fingerprint scanner of corresponding nominee.Enrolment Process and ii) Voting Process. This is the innovation we made so that no person is allowed to press voting button as it is one of the i) Enrolment Process drawbacks of the present voting machine. After the The Fig 6 shows the enrolment process clearly. completion of voting, one can know the status of theThe Process involved in using fingerprint scanner for nominees by clicking the count button.election is very simple. First, the chosen finger forexample, the thumb is captured and extracted. Thefingerprint template is then enrolled and store in alocal repository, a database. This primary process is IV. EXPERIMENTAL RESULTSdone during the registration process. After that, the In this work, we have conducted the Pilot Electionchosen finger can be live scan. The fingerprint using a Personal Computer with four fingerprinttemplate is then processed and extracted. It will scanners for selecting class representative. For that,subsequently match the scanned fingerprint against we have created the database which consists of thethe stored template. Upon verification, they will have fingerprint of the Computer Science departmentthe access to vote for their desired candidates. students with the number of 80 (45 males and 35Mismatched fingerprint certainly would indicate denial females). The database is created based on theform the access. digital personal scanner. This primary process is done during the registration process. After that, the chosen finger can be live scan. The fingerprint &ŝŶŐĞƌƉƌŝŶƚ ^ĐĂŶŶĞƌ template is then processed and extracted. It will subsequently match the scanned fingerprint against the stored template. Upon verification, they will have ĂƉƚƵƌĞ ƚŚĞ the access to vote for their desired candidates. ŝŶŐĞƌƉƌŝŶƚ ŝŵĂŐĞ Mismatched fingerprint certainly would indicate denial from the access. During the voting, the voter first places his thumb on the touch sensitive region. If the fingerprint W ŶŚĂŶĐĞŵĞŶƚ matches he is allowed to vote. In case the print is not stored before, a single beep is given, so the person cannot vote OR if the same person votes again, the DŝŶƵƚŝĂĞ džƚƌĂĐƚŝŽŶ system should give a double beep, so that the security can be alerted. The system is programmed to recognize a fingerprint twice, but to give a beep for more than once. There are three nominees for the selection of ŝŶŐĞƌƉƌŝŶƚ representative and each student is asked to vote for the candidates they wish by checking their identity ĂƚĂďĂƐĞ through fingerprint and allowing them to vote by giving thumb impression against the fingerprint Fig 6 Enrollment Process scanner of candidate. The Table 6 shows the pilot election results.ii) A Novel Design for E-Voting Process TABLE 6: PILOT ELECTION RESULT In the Fig 7, the first process is capture the input S. Name of the Count of the Votes No Candidate Polledimage, the captured image is then enhanced by usingthe best enhancement technique Gabor. The next 1 M. Jeyaraj 20step after enhancement is the extraction of minutiae. 2 S. Ashik 15After extracting minutiae, it is compared with the 3 P. Kokila 10template which is stored in the database based on 4 P. Krishna 35minutiae based matching as proposed in the previouschapter. If the matching result is true, the person is Total 80 17
  • 7. INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011 ŝŶŐĞƌƉƌŝŶƚ ^ĐĂŶŶĞƌ From the results of Table 6 it is declared that Mr.Krishna has been elected as Representative of theClass of Students. ĂƉƚƵƌĞ ƚŚĞ ŝŶŐĞƌƉƌŝŶƚ ŝŵĂŐĞ V. CONCLUSIONS AND FUTURE DIRECTION For over a century, fingerprints have been one ofthe most highly used methods for human recognition; W ŶŚĂŶĐĞŵĞŶƚautomated biometric systems have only beenavailable in recent years. This work is successfullyimplemented and evaluated four different models andPC based electronic voting system under Matlab 7.5. DŝŶƵƚŝĂĞ džƚƌĂĐƚŝŽŶThe arrived results were significant and morecomparable. It proves the fact that the fingerprintimage enhancement step will certainly improve theverification performance of the fingerprint basedrecognition system. ZĞĨĞƌƌŝŶŐ EŽƚ ůůŽǁ ƚŽ ĂůƐĞ DĂƚĐŚ The best enhancement technique Gabor is used to sŽƚĞ ĂŶĚ ŝǀĞ ŝŶŐĞƌƉƌŝŶƚ ĞĞƉ ^ŽƵŶĚ ͲŝŶŐenhance the fingerprints for electronic voting and the ĂƚĂďĂƐĞreport of the pilot study for students’ election shownthe better accuracy. By the use of this PC based dƌƵĞ ; ůůŽǁ ƚŽ sŽƚĞͿvoting system, the student’s representative is electedin a proper way with high security. Becausefingerprints have a generally broad acceptance withthe general public , law enforcement and the forensic W^ϭ EŽŵŝŶĞĞͲϭscience community, they will continue to be used with W^Ͳmany governments’ legacy systems and will be ŝŶŐĞƌƉ W^Ϯ EŽŵŝŶĞĞͲϮutilized in new systems for evolving applications that ƌŝŶƚrequire a reliable biometric. In this work, we counted the spurious minutiae and W^ϯ EŽŵŝŶĞĞͲϯdid not address impact of image enhancementalgorithm with spurious minutiae removal algorithms EŽŵŝŶĞĞͲϰ W^ϰand also we are designed only a PC based electronicvoting system. In future, we will design a device withBiometric Technology which can be used as if IndianElectronic Voting Machine. ůĞĐƚŝŽŶ ZĞƐƵůƚ Fig.7 A Novel Design for E-Voting Process ACKNOWLEDGMENT This work is a part of a Research Project and authors are thankful to UGC for funding the Project (File No. F-38-258/2009 (SR) Dt: 19.12.2009).The authors would like to thank the anonymous reviewers for their thorough reviews, and constructive suggestions which significantly enhance the presentation of the paper. REFERENCES Abishek Rawat,, A Hierarchical Fingerprint Matching System, Indian Institute of Technology, Kanpur, July 2009 18
  • 8. INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.1 JANUARY 2011Anil K. Jain and David maltoni. , Handbook of Fingerprint Rajnikannan M., Ashok Kumar D., Muthuraj, Estimating the Impact Recognition, Springer-verlag New York, Inc., Secaucus, NJ, of Fingerprint Image Enhancement Algorithms for Better USA, 2003 Minutia Detection, International Journal of ComputerAshbaugh D. R., Quantitative-Qualitative Friction Ridge Analysis: Application, No.1 Article 7, 2010 An Introduction to basic and advanced Ridgeology. CRC Raju Sonavane, Dr. B.S. Sawant., Noisy Fingerprint Image Press, 1999 Enhancement Technique for Image Analysis: A StructureBahuguna R., Fingerprint Verification using Hologram matched Similarity Measure Approach, SNS International Journal of filterings, Biometric Consortium Eighth Meeting, San Jose, Computer Science and Network Security, Vol. 7, No. 9, CA, 1996 September 2007California Internet Voting Task Force. 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