Research Article Security Enhancement for Smartphone Using ...downloads.hindawi.com/journals/ijdsn/2014/136538.pdf · Security Enhancement for Smartphone Using Biometrics in Cyber-Physical

Research ArticleSecurity Enhancement for Smartphone Using Biometrics inCyber-Physical Systems

Seung-Hoon Chae,1 Daesung Moon,2 Kyeong-Ri Ko,3 JuHyun Shin,4 and Sung Bum Pan4

1 The Research Institute of IT, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 501-759, Republic of Korea2 Electronics and Telecommunications Research Institute, 161 Gajeong-dong, Yuseong-gu, Daejeon 305-350, Republic of Korea3 Department of Control and Instrumentation Engineering, Chosun University, 309 Pilmun-daero, Dong-gu,Gwangju 501-759, Republic of Korea

4Department of Control, Instrumentation, and Robot Engineering, Chosun University, 309 Pilmun-daero, Dong-gu,Gwangju 501-759, Republic of Korea

Correspondence should be addressed to Sung Bum Pan; [email protected]

Received 6 December 2013; Accepted 11 March 2014; Published 3 April 2014

Academic Editor: Hoon Ko

Copyright © 2014 Seung-Hoon Chae et al. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited.

With the expansion of the Cyber-Physical System (CPS) concept, smartphones have come to constitute a competitive platform thatconnects humans and the surrounding physical world. Along with the communication functions and mobility of cellular phones,smartphones have various sensors in addition to greatly enhanced performances and storage space compared with existing cellularphones. However the “unlock” process of smartphones and the need for user passwords when accessing SNSs prove to be greatweaknesses in smartphone security. Therefore, smartphone security should be enhanced through biometrics, which can make upfor the shortcomings of passwords. The present study proposes minutiae-ridge based fingerprint verification for enhancing thesecurity of fingerprint verification, a biometrics, to improve smartphone security. To evaluate the proposed minutiae-ridge basedfingerprint verification performance in smartphones, its performance was compared with existing fingerprint verification methodsin terms of Equal Error Rate (EER), False Non-Match Rate (FNMR), and required number of cycles.The results show that althoughthe required number of cycles increased by 1.5%with the proposedmethod, EER andFNMR improved by 53%and 92%, respectively.

1. Introduction

Given the increased importance of the relationship of physi-cal systems existing in real spacewith cyber space, or softwarerunning on computers, the existing concept of embeddedsystems has been expanded to Cyber-Physical Systems (CPS).Various embedded devices compute based on the observedstatus of the physical system. And then, A CPS forms a feed-back system which influences the physical system itself. Inother words, it is an integrated system that observes, adjusts,and controls a physical system’s actions. As shown in Figure 1,the CPS concept covers a broad range of artificial intelligencesystems such as road traffic control, social system, and real-time information sharing [1–3].

Smartphones equippedwith various sensors have recentlywitnessed widespread adoption. They can be considered

a field where mobile CPS has been applied. The perfor-mance of mobile devices such as smartphones is improvingrapidly. Along with information processing capabilities, var-ious sensor modules such as mobile communication such ascamera, accelerometer, and gravity sensor, aswell as hardwarecapabilities such as storage space, 2nd, 3rd, and 4th gener-ation mobile communication, WiFi, Global Positioning Sys-tem (GPS), and Bluetooth enablemobile CPS throughmobiledevices. Unlike traditional embedded systems, which gen-erally are immobile given the high cost of mobility, smart-phone users can carry a mobile conveniently and acquireinformation about their surroundings using various sensors.In addition, smartphones users react to the physical worldthrough the smartphone regardless of time or space. Hence,smartphones are used as a convenient, competitive platformbetween humans and the surrounding physical world.

Hindawi Publishing CorporationInternational Journal of Distributed Sensor NetworksVolume 2014, Article ID 136538, 9 pageshttp://dx.doi.org/10.1155/2014/136538

2 International Journal of Distributed Sensor Networks

Cyberphysical system

Traffic signal

Medical device

Mobile/PC

Smart phonePerson

Server

Database

Socialsystem

Real-timeinformation

sharing

Cloudresources

rt neP

Figure 1: CPS application.

Social Networking Services (SNS) users can share theirthoughts, meditations, status, and cultures. Given that userscan share information through SNS using a smartphoneand regardless of time and space, online SNS usage throughsmartphones is increasing rapidly. Moreover, many SNScompanies have been announcing smartphone mobile appli-cations that allow quick and easy access to various SNSs [4].

The greatest strength of a SNS is that it allows commu-nication with numerous other SNS users through chatting,messaging, file sharing, wikis, e-mails, voice mails, andvideos. When communicating through SNS, in most cases,users only recognize others through their SNS IDs and not bytheir physical forms. Hence, one can impersonate another bycracking that person’s account information and logging intoa SNS using that information or by creating and operatingan alternative SNS account in that person’s name. Recently,such incidents have actually been occurring in SNSs. MostSNSs perform personal verification with a user ID and apassword, both of which are easy to steal. Furthermore, whenusing smartphones for accessing SNSs, the respective IDs andpasswords are stored in the system for automatic login. Theautomatic login feature ensures that users are not requiredto type their ID and password at each log in, as well asto avoid any inconvenience due to forgotten passwords.Furthermore, given that smartphones contain sensitive per-sonal information such as internet banking information orpictures, losing a smartphone could lead to privacy prob-lems. As communication channels can be protected usingprotocols such as HTTPS, SSL, and TLS, the weakest pointin controlling user access to web services such as SNSs is thehuman authentication, such as typing a password [5–7]. In thelight of these issues, the password-based user authenticationcurrently used in smartphones, as well as the security ofthe password-typing process, requires improvements. Rec-ognizing this need, of late, smartphone manufacturers havebeen implementing biometric features such as fingerprint orfacial verification, such as the examples shown in Figure 2,

in smartphones. Biometrics can replace not only smartphonesecurity itself but also the ID- and password-based loginmethods used currently.

The present paper proposes a minutiae-ridge based fin-gerprint verification scheme that improves upon fingerprintverification, a biometrics, for enhancing smartphone protec-tion.The proposedminutiae-ridge based fingerprint verifica-tion scheme uses fingerprint ridge information for improvingthe performance of existing fingerprint verification systems.For effectively combining theminutiae-based fingerprint ver-ification with ridge-based fingerprint verification, similaritydistribution of the former scheme was used. Minutiae-basedfingerprint verification employs the threshold values of thesimilarity of minutiae for verification. However, more errorsare detected in the region around the threshold values thanin the other regions. Therefore, the use of ridge-based finger-print verification in the region around the threshold values,where many errors occur, verification performance can beimproved. In addition, for verifying the real-time feasibilityof the proposed method on a smartphone, the size of theridge data was measured in the minutiae standard formatby executing a performance cycle. Consequently, the totaldata size increased to 1,961 bytes, but it was confirmed thatthis could be saved in the extended partition of the minutiaestandard fingerprint format file. The number of processingcycles increased by 1.5% from 5.22× 1011 cycles withminutiaeonly to 5.30 × 1011 cycles with the minutiae-ridge-basedmethod.

The thesis is composed of the following. First, themethodof strengthening smartphone security using fingerprint veri-fication is explained.Then, Section 3 introduces the proposedfingerprint verificationmethod to be applied to smartphones,and Section 4 shows the experiment results. Finally, Section 5concludes the thesis.

2. Smartphone Security Intensification UsingFingerprint Verification

More services are being provided to customers thanks to thedevelopment of computer and communication technologies,which donot impose any time or location-based limits on ser-vice delivery. Given that the importance of smartphone use isincreasing in real life owing to services such as SNS and Inter-net banking, faceless verification services are gaining impor-tance. Currently, verification systems such as password orPersonal IdentificationNumber (PIN) aremainly used. How-ever, these methods have problems such as users forgettingkey information and easy misuse by others of data exposedexternally. Several studies have focused on various solutionssolving such problems. Among those solutions, biometricshas drawn much attention as an appropriate means forsolving such problems as well as enhancing security [8, 9].

For strengthening smartphone security through finger-print verification, smartphones’ “unlock” process needs tobe strengthened. Smartphones are generally unlocked usingpasswords and patterns. When typing passwords or drawingpatterns, however, they can be easily spied on or deduced.In contrast, fingerprint-based verification is safer because

International Journal of Distributed Sensor Networks 3

(a) (b) (c)

Figure 2: Biometrics in smartphones: Face unlock method of Android, Touch ID of iOS, and Biometric TrackPad of CrucialTec.

even if someone tries to spy on the fingerprint input process,fingerprint information cannot be leaked or deduced. More-over, because the user simply has to touch the fingerprintsensor, the inconvenience resulted from the typing passwordwhen using SNSs is avoided. Even smartphonemanufacturersare implementing fingerprint sensors in their products ordeveloping fingerprint sensors for smartphones for improv-ing smartphone security. Hence, the security of the passwordinput process, which is the weakest link in controlling useraccess to smartphones or SNSs, can be improved by usingfingerprint verification, as shown in Figure 3. Moreover, thismethod can be used not only for smartphone unlocking andaccess but also for user verification in various smartphoneapplications.

3. Proposed Fingerprint Verification System

The minutiae-based fingerprint verification method is themost commonly used fingerprint verification method. Giventhat this method is very effective for comparing fingerprintsand the required data size is small, a verification systembased on it can be miniaturized and would be good for real-time processing. This is also the reason for the method’swidespread use. However, the minutiae-based fingerprintverificationmethod is plagued by low performance in regionswhere preregistered and current input fingerprints overlap orwhen the input fingerprint image is small and the minutiaeextracted are insufficient for verification.These problems leadto the occurrence of faulty matching, thus rejecting autho-rized persons. To avoid faulty matching and improve verifi-cation performance, many researchers have investigated thepreprocessing of fingerprint images for improving fingerprintverification performance, enhancement of the registeredtemplates’ quality, adoption of fingerprint matching methodsthat can use the extracted templates effectively, and templateprotection for preventing leaks of registered templates. Manystudies on methods for overcoming the problems associatedwith the fingerprint matching method using direction filters(instead of minutiae), phase information, and fingerprintimages have been carried out [10–15].

In this study, experiments were conducted for improvingfingerprint verification performance using fingerprint ridges.In addition, the integration of minutiae and ridge-basedfingerprint verification using the partial band is proposed forefficient use of the proposed fingerprint verification schemein smartphones.

3.1. Minutiae-Ridge Based Fingerprint Verification Algorithm.Among the various fingerprint verification methods, theminutiae-based fingerprint verification method offers excel-lent security and convenience. Additionally, because themethod required small-sized data and a small fingerprint sen-sor, a system based on it can be miniaturized easily. However,there is the problem of faulty matching due to fingerprintsensor miniaturization. The method’s performance of themethod can be improved by creating high quality minutiaebut only up to a certain extent. In this study, the performancewas verifiably improved by using ridge information fromfingerprints. Because fingerprint data is not aligned, a processfor aligning the data of two fingerprints is required. Methodsfor aligning fingerprint images include ridge-based methodsand minutiae-based methods that use the cores or deltasof two fingerprints [16, 17]. However, because image-basedmethods require a lot of calculation and there are caseswhen there are no minutiae such as core or deltas in theinput images, their use is limited.Therefore, the image-basedfingerprint data alignment process is less efficient than theminutiae-based method.

The ridge-based fingerprint verification method used inthis paper takes more time than the minutiae-based system.If the ridge information is calibrated using the compensationprocess of minutiae-based fingerprint matching, the analysistime required for ridge-based fingerprint matching can bereduced. Furthermore, the performance of the minutiae-based fingerprint verification is superior when there is highsimilarity of minutiae, but verification errors occur aroundthreshold values with low similarity. The area around thethreshold values is called the partial band. This paper pro-poses a hybrid fingerprint verification system that does notuse ridge-based verification in areas with high confidence but


Unlock Access

Figure 3: Security reinforcement through biometrics.

uses it for fingerprints with minutiae similarity in the partialband area. Therefore, ridge-based fingerprint matching wasconducted using minutiae-based similarity and compensa-tion information. Figure 4 shows the proposed minutiae-ridge based fingerprint verification system proposed in thispaper.

This system determines whether to perform ridge-basedfingerprintmatching after conductingminutiae-based finger-print matching. For minimizing the computational burdendue to the execution of ridge-based fingerprint matching,ridge-based fingerprintmatching is used in a limitedmanner,as determined by the partial band. If there is minutiaesimilarity within the partial band, which requires ridge-basedverification, then ridge-based verification is performed andthe result is used for verification.However, if there isminutiaesimilarity in areas except the partial band, where the resultsof minutiae-based fingerprint verification are reliable, onlyminutiae-based verification is used for verification. Whenperforming ridge-based fingerprintmatching, the compensa-tion stage uses compensation information from theminutiae-based fingerprint matching results. By combining minutiae-based matching, which lacks verification information andfaces difficulties in the compensation stage, with ridge-basedfingerprint matching, a fingerprint verification system thatdelivers improved performance in real-time is realized. InSections 3.2 and 3.3, the ridge-based fingerprint matchingscheme and the partial band are described.

3.2. Ridge-Based Fingerprint Matching Method. Ridges arebasic elements of fingerprints, and their minutiae such asbifurcation points and ending points are decided accordingto the bifurcations of ridges and their ending forms. Thus,ridges are components containing fingerprint verificationdata. The ridge images used in this study were thinned

through preprocessing for easier selection. The fingerprintridge images obtained via thinning have a thickness of 1 pixel.

The thinned ridge images can effectively and simplyexpress fingerprint images. Therefore, the capacity require-ment of the template fingerprint images can be reduced. Inaddition, the minutiae are distributed randomly. However,because the ridges are distributed evenly and contain dataof entire fingerprints, much of the data that can be obtainedfrom the overlapped area of two fingerprint images is small.This paper uses data on the distance between two ridges forcomparison. It can reduce errors in cases where two finger-prints cannot be fully matched or where fingerprints aredistorted, as in Figure 5, as distance is used.

The ridge-based matching method used in this paperconsists of three stages: stage of generating comparing points,compensation stage, and distance calculation stage betweencompared points. The ridge image used in the matching stepis a thinned fingerprint image. The stage of generating com-paring points is where comparing points are generated at reg-ular intervals on a ridge by tracking the ridges in said image.The advantage of doing so is that it reduces the differencebetween the computed and measured distance by inputtingall coordinates of the ridge into the comparing points. Theexperiment in this paper generated comparing points atintervals of 10 pixels.

In the compensation stage, the distance between thecomparing points of registered fingerprints and input fin-gerprints is measured. The comparing points of two fin-gerprints are placed in the same topological space and thedistance between the comparing points is measured. Simi-larity between two ridges is obtained by using the distancesbetween pairs of comparison points on each ridge. The meandistance of a pair of comparison ridges, standard deviation,and the number of pairs of comparison points are obtained


Inputfingerprint

Registrationfingerprint

Minutiae-basedmatching Do the score included

in partial bandRidge-basedmatching

Verification

Yes

No

Figure 4: Proposed minutiae-ridge based fingerprint verification system.

(a) Undistorted ridge (b) Distance between ridges

Figure 5: Nondistortion error.

based on the distance value calculated. The data obtainedby measuring the distance between ridges and the numbersof pairs of whole corresponding ridges existing in the twofingerprint images are used for verification:

𝑆𝑅=

∑𝑁−1

𝑛=0𝑅 (𝑛)

𝑅MAX× 100. (1)

𝑆𝑅, or similarity between the two fingerprints, is calculated

according to (1). 𝑅(𝑛) is the score of pairs of the 𝑛thcorresponding ridges of 𝑁 ridges, and 𝑅MAX is the maxi-mum number of comparison points. The ridge pairs’ scoreapproaches 1 as the standard deviation of mean distancedecreases and approaches 0 because the mean distance has ahigher standard deviation. Additionally, because confidenceis low when the number of comparison ridges is less than 10,a low weight is assigned.

Figure 6 presents the Receiver Operating Characteristics(ROC) of ridge- and minutiae-based fingerprint verification.As shown in Figure 3, we confirmed that ridges can beused for fingerprint verification. The Zero False-Match Rate(ZeroFMR) of the ridge-based verificationmethod was lowerthan that of minutiae-based verification.

3.3. Partial Band Setting. Ridge-based fingerprint matchingrequires more computation than minutiae-based fingerprintmatching. Thus, it is necessary to limit its use duringfingerprint matching using the partial band. This partialband is determined through fingerprint minutiae similaritydistribution. If there is minutiae similarity within the partial

0.0001

0.001

0.01

0.1

1

0.0001 0.001 0.01 0.1 1

FNM

R

FMR

MinutiaeRidgeEER

Figure 6: ROC of ridge- and minutiae-based fingerprint verifica-tion.

band, which requires ridge-based verification, then ridge-based verification is performed and the result is used for ver-ification. However, if there are minutiae similarities in areasother than the partial band, where the results of minutiae-based fingerprint verification are reliable, only minutiae-based verification is used. Therefore, the partial band has asignificant influence on fingerprint verification performance.Figure 4 presents the similarity distribution of the minutiae-based fingerprint verification system.


0102030405060708090

100

1 11 21 31 41 51 61 71 81 91 101

Rate

(%)

Score (%)

GenuineImposter

False nonmatchFalse match

𝛼 𝛽

Si Sg

th

Figure 7: Similarity distribution of minutiae-based fingerprintverification.

Errors of the minutiae-based fingerprint verificationmethod are distributed centering on the threshold value, asshown in Figure 7. These errors occur when genuine users’fingerprint similarities are below the threshold value andwhen impostors’ fingerprint similarities are above the thresh-old value. They are called false nonmatch and false-match,respectively. This study used the partial bands between sim-ilarity 𝛼, where false nonmatch is 0, and similarity 𝛽, wherefalse-match is 0, as themaximumbands, as shown in Figure 4.𝑆𝑖and 𝑆

𝑔have similar values in between 𝑡ℎ and 𝛼, and 𝑡ℎ

and 𝛽, and similar ranges of the partial band. Because impos-tors’ and genuine users’ similarity distributions are similararound the threshold value within the partial band, thatis wheremost errors occur. If ridge-based fingerprint verifica-tion with excellent false-match resistance can be performed,verification performance can be improved.

Because ridge-based fingerprint verification uses a con-siderably large amount of comparison data, false-matcherrors can be reduced. However, its Equal Error Rate (EER)performance is lower than that of the minutiae-based veri-fication method because rejection errors occur owing to thedistortion of fingerprint images. When ridge-based verifica-tion is conducted within the partial band of the thresholdvalue where minutiae similarity exists, the distribution issimilar; that is, the gain from decreased false-match errors isbigger than the loss from rejection errors.

4. Experimental Results

This paper conducted a test using a DB of Set A fingerprintsof FVC 2002 [18]. Tests of genuine users and impostorswere conducted 2,800 and 4,950 times, respectively. For thepartial band of the minutiae for in ridge-based fingerprint

Table 1: Fingerprint verification performance based on the value ofP.

Partial band ratio (%) FMR (FNMR = 3.25%) EER (%)0 2.77 3.0110 2.02 2.220 1.35 1.9230 0.79 1.6840 0.42 1.450 0.22 1.460 0.44 1.7270 0.61 1.9480 1.25 2.0790 2.04 2.22100 2.63 2.48

verification, similarity distribution was used. The minutiaesimilarity partial band with 𝑃 distribution was selectedaccording to (2):

𝑃 =

𝐼 (𝑡ℎ, 𝑠𝑖) + 𝐺 (𝑡ℎ, 𝑠𝑖)

𝐼 (𝑡ℎ, 𝛼) + 𝐺 (𝑡ℎ, 𝛼)

× 100

=

𝐼 (𝑡ℎ, 𝑠𝑔) + 𝐺 (𝑡ℎ, 𝑠𝑔)

𝐼 (𝑡ℎ, 𝛽) + 𝐺 (𝑡ℎ, 𝛽)

× 100,

0 ≤ 𝑡ℎ ≤ 100.0,

𝑠𝑖 = 𝑡ℎ, 𝑡ℎ − 1.0, 𝑡ℎ − 2.0, . . . , 𝛼,

𝑠𝑔 = 𝑡ℎ, 𝑡ℎ + 1.0, 𝑡ℎ + 2.0, . . . , 𝛽,

𝐼 (𝑖, 𝑗) : Number of impostor fingerprints

in similarities 𝑖 and 𝑗,

𝐺 (𝑖, 𝑗) : Number of genuine fingerprints

in similarities 𝑖 and 𝑗.

(2)

The compensation values used for ridge-based fingerprintverification in the experiment were from the top three candi-dates with the highest similarities among the compensationvalues of minutiae-based fingerprint matching. The numberof ridges used for comparison in one fingerprint image andthe average number of comparison points extracted perridge were 37 and 13, respectively. That is, this study testedfingerprint verification performance after finding similarityvalues below and above the threshold with the number offingerprints of proportion 𝑃 based on the threshold value.

Table 1 summarizes fingerprint verification performancebased on 𝑃. The verification results were obtained in thepartial bands between 0% and 100% of 𝑃, and performancewas confirmed.

Table 1 lists the results of the test using only the minutiae-based verification system when the 𝑃 of the partial band was0. To confirm the improvement in and reduction of false-match errors, the FNMR was fixed and the changes in theFMR were tested. For evaluating verification performance,EER was measured. As a result, when the FNMR was fixed


Type X coordinate RFU Y coordinate

2bits 2bits14bits 14bits

Minutiae type X location Reserved Y locationAngle, 0–255 Quality, 0–100

1byte 1byte

Minutia angle Minutia quality

2bytes 2bytes

(a) Finger minutiae data

Block length Type ID code Length Data · · · · · · · · · · · ·

2bytes 2bytes 2bytes (“Length”-4) bytes

“Block length” bytes

Extended data Extended data Extended Extended datablock length type code data length

(b) Extended data

Figure 8: Standard format of finger minutiae data.

Ridge # Ridge data

X coordinate Y coordinateReserved Reserved · · · · · ·

4bytes

14bits14bits

1byte

2bits2bits

Figure 9: Ridge data structure using standard minutiae format.

Table 2: Hardware capacity by matching method.

Methods Average cycles Average data sizeMinutiae-based 66,074 cycles 192 bytesRidge-based 873,113 cycles 1,961 bytes

at 3.25%, its FMR performance was better than that with theminutiae-based verificationmethod. In particular, it was con-firmed that it improved from 2.77% to 0.22%when 50%of thepartial band was used. Furthermore, the EER of the proposedmethod was improved from 3.01% to 1.4%. Through the testresults, it was confirmed that the proposed verification systemimproved the FMR and verification performance.

Regarding the standard format of finger minutiae speci-fied by ISO/IEC, the structures ofminutiae data and extendeddata are shown in Figure 8 [19]. In this paper, data structur-ing, as shown in Figure 9, was used for applying the ridge datato the standard format.

Using the data structures shown in Figures 8 and 9,the average data sizes of FVC 2002DB used in this studyfor minutiae and ridges were calculated, and the cyclesfor performing fingerprint verification were measured. Theresults are listed in Table 2.

The average number of minutiae extracted from thefingerprints used in the experiment was 32. To determinethe average data size of the minutiae based on Figure 8(a),a total of 192 bytes, which is 32 (number of minutiae) ×6 (data size per minutiae) is used. In this paper, 37 ridgeswere extracted for one fingerprint, and then 13 comparisonpoints were extracted per ridge. If the ridge data is storedin the same way as in Figure 9, its size would be 1,961 bytes.Therefore, the data size of ridge information is approximately10 times the data size of minutiae. The size of extendeddata, which is supported in the standard format, as shown inFigure 8(b), is 65,531 bytes. Therefore, the average 1,961 bytesof ridge data can be saved in a standard fingerprint format file.In addition, when performing fingerprint matching, severalcompensation values are used. Therefore, the number ofaverage cycles of minutiae-based fingerprint matching usedin this experiment to match one fingerprint is 67,423,757.Because the top compensation values used in minutiae simi-larity are used for ridge-based fingerprint matching, approxi-mately 2,619,341 additional cycles are needed. In other words,if ridge-based fingerprint matching is used in the fingerprintverification systemproposed in this paper, the required cycleswill be 3.8% higher than the cycles needed in minutiae-basedfingerprint matching.Therefore, themethod proposed in this


Table 3: Number of cycles required for testing.

Method Number of cyclesMinutiae-based 5.22 × 1011 cyclesThe proposed method (P = 50) 5.30 × 1011 cycles

paper can enhance fingerprint verification performance andperform verification in real-time.

Table 3 lists the number of cycles generated in the processof performance measurement in Table 1. For 2,800 genuineuser attempts and 4,950 impostor attempts, the total numberof cycles of minutiae-based fingerprint matching is 5.22 ×1011. However, in the method proposed in this paper, if thepartial band is set as 50%, the number of cycles increases to5.30 × 1011. This is a 1.5% increase compared with the caseof minutiae-based fingerprint matching. Based on this result,if the method proposed in this paper is used, it is confirmedthat although the computation burden increases by 1.5%, theverification performance is improved by 53%. Additionally,ridge information can be stored by using the extendedpartition of existing minutiae standard data format, andthen verification can be performed.

5. Conclusions

Lately, the utilities of smartphones are greatly increased. Theuse of SNSs, which are used for sharing personal informationand status through web services, is increasing in conjunctionwith the spread of high-performance smartphones. SNSs arefundamentally changing not only personal life patterns butalso modern political, economic, and social environments.SNSs can replace conventional media that can not performits role properly by the control in countries. Elsewhere,they act as alternative media outlets. Hence, SNS securityis very important. When accessing SNSs, especially, throughsmartphones, password information can be leaked during theuser login process or SNSs can be hacked if one loses theirsmartphone. The present study proposed using fingerprintverification for improving security of smartphones as well asthat of the SNS login process. In addition, it proposed theuse of fingerprint ridges for improving the security of exist-ing fingerprint verification systems. Furthermore, the studyproposed the use of minutiae- and ridge-based fingerprintverification using the partial band for effectively improvingfingerprint verification performance within the limited envi-ronment of a smartphone. Experiments using the proposedfingerprint verification method showed that the FMR wasreduced from 2.77% to 0.22%, or by 92%, and EER wasimproved by 53% over minutiae-based verification. In addi-tion, for evaluating the computation load of the verificationmethod, the number of cycles required for a pair of finger-prints and the number of cycles required for the entire fin-gerprint verification experiment were measured. As a result,the number of cycles in the proposed method increased by3.8% and 1.5%, respectively, compared with the minutiae-based fingerprint matching. However, it was confirmedthat the verification performance increased by 53%. In future,

we will study the efficient storage of ridge data and its optimaluse in small systems.

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper.

Acknowledgment

This study was supported by research fund from ChosunUniversity, 2013.

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[16] A. N.Marana andA. K. Jain, “Ridge-based fingerprintmatchingusing hough transform,” in Proceedings of the 18th BrazilianSymposium on Computer Graphics and Image Processing (SIB-GRAPI ’05), pp. 112–119, October 2005.

[17] A. K. Jain, S. Prabhakar, L. Hong, and S. Pankanti, “Filterbank-based fingerprint matching,” IEEE Transactions on Image Pro-cessing, vol. 9, no. 5, pp. 846–859, 2000.

[18] FVC, 2002 Database, http://bias.csr.unibo.it/fvc2002/databases.asp.

[19] ISO/IEC JTC1/SC37 IS, “Biometric data interchange formatspart 2: finger minutiae data,” Tech. Rep. 19794-2, 2005.

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Research Article Security Enhancement for Smartphone Using ...downloads.hindawi.com/journals/ijdsn/2014/136538.pdf · Security Enhancement for Smartphone Using Biometrics in Cyber-Physical