(2007) Image Quality and Minutiae Count Comparison for Genuine and Artificial Fingerprints

Image Quality and Minutiae Count Comparison

for Genuine and Artificial Fingerprints

Stephen J. Elliott Ph.D., Shimon K. Modi, Lou Maccarone, Matthew R. Young, Changlong Jin, andHale Kim Ph.D.

Abstract-The vulnerabilities of biometric sensors have beendiscussed extensively in the literature and popularized in films andtelevision shows. This research examines the image quality of anartificial print as compared to a genuine finger, and examines thecharacteristics of the two, including minutiae counts and imagequality, as repeated samples are taken.

Keywords-authentication, biometrics, fingerprint recognition,repeatability.

I. INTRODUCTION AND MOTIVATIONV ERIFYING the identity of an individual can beVaccomplished by three main considerations: what an

individual has, what an individual knows or owns, and whatan individual is. The first option is typically achieved throughthe use of a token, such as an identification card, badge,magnetic stripe, or radio frequency identification (RFID) tag.The second option can be achieved through the use of apassword or personal identification number (PIN). The thirdoption can be accomplished through what an individual is byutilizing biometric technologies. Like the first twoauthentication methods, biometric systems containvulnerabilities and are susceptible to attack. Some of thesevulnerabilities are similar or even overlapping across all threeauthentication mechanisms. However, attacks specific tobiometric systems focus on liveness detection of a human (i.e.,whether the finger from a live sample or a gelatin sample).Various attacks documented in the literature have focused onthe sensor [11, [2]. While understanding and preventingattacks on the sensor is an interesting research topic worthy ofinvestigation, this paper examines the global and local featuresof a live sample compared to that of a gelatin finger from the

S. J. Elliott, Ph.D. is an Associate Professor with the Departnent ofIndustrial Technology at Purdue University, West Lafayette, IN 47907 USA;(e-mail:elliottapurdue.edu .

S. Modi is graduate student with the Departnent of Industrial Technologyat Purdue University, West Lafayette, IN 47907 USA; (e-mail:[email protected]).

L. Maccarone is an undergraduate student at Purdue University, WestLafayette, IN 47907 USA

M. R. Young is a graduate student with the Department of IndustrialTechnology at Purdue University, West Lafayette, IN 47907 USA

C. Jin is a graduate student with the School of Information andCommunication Engineering at Inha University, Incheon, Korea; (e-mail:[email protected]).

H. Kim is a Professor with the School of Infornation and CommunicationEngineering at Inha University, Incheon, Korea; (e-mail:[email protected]).

1-4244-1129-7/07/$25.00 ©2007 IEEE

same user after acquisition on a commercially availablebiometric fingerprint sensor. This research seeks to answerwhether the samples (live versus gelatin) exhibit the sameminutiae counts and whether the acquired images possess thesame image quality properties. That is, are the live and gelatinsamples similar in their characteristics, and do theirsimilarities or differences have an impact on matchingperfonnance?

II. BIOMETRIC SYSTEM VULNERABILITIESAll security measures, including mechanisms for

authenticating identity, can be circumvented. The processesassociated with working around these measures vary indifficulty according to the level of effort and resources neededto cany out the deceptive act. Authentication mechanismsbased on secrets are particularly vulnerable to "guessing"attacks. Token mechanisms that rely on the possession of anobject, typically a card or badge technology are mostvulnerable to theft or falsified reproduction. Biometrictechnologies closely tie the authenticator to the individualidentity of the user through the use of physiological orbehavioral characteristics. While this property offers an addedadvantage over the other two authentication mechanisms, itplaces a great emphasis on validating the integrity of thebiometric sample acquired and transferred in the biometricsystem. Ratha, Connell, and Bolle provided a modelidentifying vulnerabilities in biometric systems [3]. Anexample of the threat model is shown in Fig. 1, and builds onthe general biometric model outlined in Mansfield andWayman [4].

Fig. 1 Biometric system threat model

The biometric system threat model shown in Fig. 1 contains11 individual areas of vulnerability. In addition to the fivemain internal modules characterized in the general biometricmodel (data collection, signal processing, matching, storage,

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and decision) [4], another component is added to represent thetransfer of the authentication decision to the application thatrelies on the decision from the, biometric system. Suchapplications could be identity management systems (IDMS) oraccess control systems for logical and or physical access toresources. These systems can vary in complexity and size,ranging from a local computer log-in all the way to a wide-scale distributed architecture seen in the cases of the U.S.Department of Transportation's Transportation WorkerIdentification Credential (TWIC) [5] or the Personal IdentityVerification (PIV) of Federal Employees and Contractors [6].The remaining points of vulnerability are communicationchannels between these six modules. It is worth noting that notall 11 vulnerability points are unique to the biometric system.Many of the same points (e.g., storage and communicationchannels) are vulnerable in other authentication systems andsimilar methods can be used to limit those particularvulnerabilities.

The most publicized vulnerability in biometric systemsresides at the data collection module in the form of spoofmg orpresenting artificial representations of biometric samples (seemodule #1, Fig. 1). If an artificial or fake biometric sample isaccepted by the biometric system at this initial stage, the entirebiometric system is corrupted and the system is compromised.Attacks on biometric systems are not new, popular cultureseeks to circumvent security systems and biometric systemsare not immune. Several online resources are available thatdescribe such attacks on the data collection module, and manymovies and television shows have highlighted attacks on suchsystems. One such attack at this data collection module wasoutlined in the work of Matsumoto, Yamada, and Hoshino(2002) using "Gummy Fingers" [1].The biometric research community, as well as industry, has

focused its research on preventing such attacks by using theconcept of "liveness" detection techniques. Today, the newersensors are improving their resilience against a spoofing attackat this module. In the past, acetate spoofing attacks - wherean image of a fingerprint placed on acetate was accepted as agenuine live finger - was easy to reproduce. Now, suchattacks are proving increasingly difficult to succeed, hence themore complicated approaches to attacks being waged on thevulnerable sensor. Techniques for liveness detection withinthe fingerprint modality focus on moisture content,temperature, electrical conductivity, and challenge response.

III. FINGERPRnT IMAGE QUALITY ANALYSISThe purpose of this research paper was not to prove the

vulnerability of the biometric system, but to examine therepeatability of the features of the gelatin finger print ascompared to the live genuine sample once the image has beenacquired. The research question is whether an artificial printcaptured on an optical sensor exhibits any of the samecharacteristics as a genuine fingerprint from the sameindividual captured on the same sensor, and whether anydistinguishers might enable the artificial print to be excludedlater on in the process, if the initial data collection moduleaccepts it. The research also investigates whether, over time,

the features of the two fingerprints remain consistentRepeatability of the extracted features is important for thematching process in any type of biometric technology [7]. Thefeatures to be examined include: minutiae points and imagequality. One of the challenges associated with this research isto ensure the image is of sufficient quality. A wide variety offactors can influence the quality of fingerprint samples. Non-uniform contact, inconsistent contact, or irreproducible contactwith the fingerprint sensor can result in images with a lowsignal-to-noise ratio, which is not desirable for featureextraction and matching purposes [9]. Wear and tear of theskin and aging effects can semi-permanently alter ridgecharacteristics. These factors also affect acquisition offingerprints by the fingerprint sensor. The importance ofquality is widely acknowledged, but there is no standardmeans of assessing quality. The current standardization effortfor assessing quality for biometric samples refers to threedifferent connotations of quality:

* Character* Fidelity* Utility

These three connotations ofbiometric sample quality can bedirectly applied to fingerprint sample quality. Character is adescription of quality based on inherent features from thesource of the fingerprint. Individuals who have scarredfingerprints or dry or cracked skin on the fingertips willprovide samples with poor character. Fidelity is a descriptionof quality based on degree of similarity between the actualfingerprint and the fingerprint image acquired by the sensor.Inconsistent contact with the fmgerprint sensor can lead tofingerprint samples with poor fidelity. Utility is a descriptionof quality based on observed or predicted contribution of thefingerprint sample to the overall performance of thefingerprint recognition system. Utility of a fmgerprint sampleis directly affected by the character and fidelity of thefingerprint sample, and should be the closely related toperformance ofthe recognition system.A substantial amount ofresearch has been conducted in area

of quality assessment, all of which give varying levels ofimportance to character, fidelity, and utility. Previous researchin the field of fingerprint image quality assessment can begeneralized into three categories: local features analysis,global features analysis, and quality analysis as a classificationproblem [10]. Features of the fingerprint image such asminutiae count, fidelity of minutiae, contrast ratio betweenridges and valleys, capture area of the figerprint, anddetennination of dominant direction are used by qualityalgorithms in varying capacities to make qualitydeterminations. For the purpose of this research, fingerprintsfrom live fngers and gelatin fingers were examined by twodifferent image quality algorithms, one provided by Aware,Inc. and the other provided as a part of NIST FingerprintImaging Software (NFIS).

IV. METHODOLOGY

This research involved two separate experiments. The first

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experiment was conducted in two stages. Stage 1 of the firstexperiment involved creation of a set of images from anartificial gelatin fimger that was crafted from the same subjectwho provided the genuine fingerprint. The procedures toaccomplish this feat required adaptation of several differentmethodologies outlined in the literature for creating anartificial fingerprints, including the work done by Matsumoto,Yamada, and Hoshino [1]. Prior to creating the mold, thefollowing necessary ingredients and utensils were gathered:plastic clay, hot water, and a pair of plastic tongs. To createthe mold, a quantity of plastic clay sufficient to cover thegenuine finger was required. In order to make the plastic claymalleable, it was placed briefly in boiling water. In order to beutilized, the plastic clay had to have a consistency such that itenabled a mold to be created by placing the finger with onlylight pressure. When the plastic clay has attained a sufficientlevel of pliability, the clay was cooled. Once the clay hadcooled enough to be touched, the finger was placed into theclay to a depth sufficient to create the mold to be used to craftthe gelatin finger. The genuine finger was kept in the plasticclay until the clay had cooled enough to retain its shape andthe details of the genuine finger. After the finger wasremoved, the mold was allowed to cure for an additional 10minutes. The resulting mold for this study is shown in Fig. 2.

Fig. 2 Plastic mold formed to create gelatin finger

The next step was to create a gelatin mixture capable ofproducing artificial fingerprints from the mold that would berecognizable to the sensor. Two sheets of gelatin weighing3.5g were soaked in cold water for five minutes. In order toremove the excess water, the gelatin sheets were dried untilthe gelatin weighed 14-16g. Next, a bowl was immersed intoextremely hot water, and the gelatin was placed into the bowlto soften and melt the gelatin. Once the gelatin had melted, itwas poured into the clay mold. Immediately after placing thegelatin in the mold, the mold was placed in a refrigerator tocool for 10 minutes at a temperature of 1°C. Coolingtransforms the gelatin to a state that is resistant to changes inshape when touched. Ambient room temperate, when thisexperiment was conducted, was 220C.

After the gelatin finger had cured in the refrigerator for aperiod of approximately one hour, it was removed from theclay mold and placed on the sensor to determine whether itwas actually able to produce images. Ten attempts to acquirean image were made; in all 10 instances, an image wasproduced. The software verified the mold. If the mold was not

verified by the software, then the mold would have beendestroyed and the process started again.The artificial finger was returned to the refrigerator in a

simple (airtight, but not vacuum-sealed) plastic storagecontainer for 48 hours at a temperature of 2TC. This procedureallowed the gelatin finger to completely solidify to itspermanent state.

After removing the gelatin finger from the refrigerator, testswere conducted on it to estimate the optimal load required foracquiring images. In general terms, it is best to use the leastweight possible to produce a scan in order to minimize thespreading and dissolution of the gelatin finger's valleys andridges. Testing of loads ranging from 20Og to 1,000g, asmeasured by a Tanita digital scale, was performed.Approximately 200g was determined to be the lower limit toproduce an image, with 550g being the upper limit beforedistortion and inability to match occurred. The next stage ofthe experiment involved acquisition of a series of images fromthe gelatin finger. All of the images were acquired from theoptical sensor using the gelatin fimger over a 15-minute timeperiod. After 15 minutes of acquiring images, the gelatinfinger had degraded to the point at which it was no longer ableto be accepted by the optical sensor. In all, 163 images wereproduced over the 15-minute time period. A detaileddescription of the 160 images utilized in the study is providedin the results section.

Stage 2 of the first experiment called for the collection of aseries of live samples. One hundred sixty live samples wereacquired from the same finger that was used to create theartificial gelatin finger. The live samples were collected overan 8-minute time period on a commercially available opticalsensor. The authors chose to collect 160 live samples, as thiswas the same number of fingerprints collected from the gelatinfinger. These 160 live images were all stored according to thetime collected; they are used to provide a baseline qualityassessment that will be compared against the samplesgenerated by the gelatin finger.

After data collection, both sets of images (from the livefinger and the gelatin fimger) were processed through the NISTFingerprint Image Software (NFIS) package. The MINDTCTfunction was used to count the number of minutiae present ineach individual image. The NFIQ function was used toevaluate image quality, which is determined on a rating scaleof 1 to 5, with 1 being the best and 5 being the worst. Theresults of M1NDTCT and NFIQ from both groups (the livefingerprints and the gelatin fingerprints) were then comparedby the means of statistical t-tests (using an a level of 0.05) todetermine if any statistical difference existed across thegroups. Aware, Inc. offers a commercially available imagequality and minutiae count software; this software was used toextract image quality scores and minutiae counts forfingerprints from the live finger and gelatin finger groups. Byutilizing two different software packages to analyze thefingerprints, we sought to eliminate bias that might have beengenerated by utilizing only a single application.The second experiment involved collecting fingerprint

images from left index finger and the right thumb from 30different subjects. Each subject was asked to provide 20

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images of each fimger on three different optical sensors. Asilicon mold was created the left index and right thumb ofeach subject, and the molds were used to provide 20fingerprint images on each of the three optical sensors. Theprocedures used in [1] were also employed in creating thesilicon molds in the second part of the experiment. At the endof the data collection there were 3600 fingerprint images from

live fingers, and the 3600 fingerprint images from siliconfingers.

V. RESULTS

A. Experiment ICreation and acquisition of images from gelatin fingers can beproblematic, as previous research has shown that gelatinfingers do not afford consistent repeatability. However, thisstudy provides anecdotal evidence suggesting that betterpreparation and storage of the artificial finger can aid in therepeatability of the images produced. The first 39 samplesprovided consistently successful spoofing results; on the 40thpresentation of the gelatin finger, a failure-to-acquire (FTA)resulted. Overall, 163 images were acquired, but only 160images were used for the final study. Degradation on the final3 images rendered the images unusable. The acquisition ratefor this particular gelatin fingerprint was 90.7%, producing aFTA rate of 9.3%. Fig. 3 shows the gelatin print (left) a liveprint (right). The FTA rate for the live finger was 0.0%.

Fig. 3 Gelatin finger (left) and live fmger (right)

The minutiae count analysis on both the fingerprint groups

was performed. Fig. 4 and Fig. 6 show box plots of theminutiae count from the live finger and gelatin finger,

respectively, generated from Aware, Inc.'s image quality tool.Fig. 5 and Fig. 7 show box plots of the minutiae count from

the live finger and gelatin fmger, respectively, generated from

NFIS's MINDTCT.The results from the box plot graphs generated by both the

Aware and NFIS software programs show that the livefingerprints have a lower minutiae count than the gelatinfingerprints, which is most likely a result of indirect andinconsistent contact with the optical sensor. In order to studythe deterioration of the gelatin fingerprints, the first 16 and last16 samples from the live and gelatin fmgerprint groups were

used. Fig 6 shows a box plot ofthe live and gelatin fingerprintminutiae count for first 16 prints, and Fig. 7 shows a box plot

of live and gelatin fingerprint minutiae count for last 16 prints.An interesting observation is that the minutiae count increases

for the gelatin fingerprint group, but stabilizes for livefingerprints.

a

AWARE Live ca,t AWARELGelItkSem_t

Fig. 4 Box plot, live and gelatin finger minutiae count using AwareQualityCheck

110

90-

900a0-

60-o

60

50

40

NRSLve_Cort NFS.Geeatincount

Fig. 5 Box plot, live and gelatin finger minutiae count using NFISM1NDTCT

The stabilization of minutiae count for the live fingerprintscan probably be attributed to habituation or acclimation to the

device. The subject has been acclimated to placing the samplefinger on the optical sensor, which reduces the inconsistentcontact of finger surface with the platen of the sensor. Anotherinteresting observation is the increase in minutiae countbetween the gelatin fingerprints over time. This suggestsdegradation of the gelatin finger and mold because ofrepetitive use and introduction of cracks in the mold used tocreate the gelatin finger. Evidence suggests that, over time, thenumber of minutiae for the gelatin fingerprint increases, whilethe number of minutiae for the live fmgerprint stabilizes.

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Boxplot ofAWAREJLive-comt, AWARE_Gelatincount70.

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Boxplot of Lisfe_First-16;, Gelatin-First-16

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Fig. 6 Box plot, live and gelatin finger minutiae count using Aware,first 16 prints

Boxplot of Live_Last_6, GelatinjLastL1665-

60-

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40-

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30

Live_Last_16 Gelati_Last_16

Fig. 7 Box plot, live and gelatin finger minutiae count using AwareQualityCheck, last 16 prints

Fig. 8 and Fig. 9 shows the scatter plots for minutiae countversus sample numbers of live and gelatin fingerprint groups.Both of these graphs give credence to the observations madefrom the box plots.

Fig. 8 Scatter plot, minutiae count vs. sample number using NFISMIINDTCT

Scatterplot ofAWAREJive_ount, AWARE_Gelatincmnt vs bnaJeCoLnt70 ,V,able

l AWARE Live count_ -_U- AWAREfidati count

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Fig. 9 Scatter plot, minutiae count vs. sample number using Aware

Image quality is another metric that was consideredimportant for this research. Fig. 10 shows the scatter plot ofimage quality scores obtained using Aware, Inc.'s qualityalgorithm on the live and gelatin fingerprints. The graph ofimage quality scores clearly indicates a degradation of thegelatin fingerprint. T-tests of image quality scores between thelive and gelatin fingerprints showed a statistically significantdifference. The severe decrease in image quality noticed in therepeated use of the gelatin finger indicates that it would be ofpractical use only for the first 10 or so attempts. Table 1 showsthe results from the t-tests.

Fig. 10 Scatter plot, image quality scores using Aware QualityCheck

TABLE IT-TEST RESULTS

Groups N Mean p-valueAware_LiveLIQ 160 79.22 <.05Aware_Gelatin_IQ 160 61.0'NFIS_Live-IQ 160 1.88 <.05NFIS Gelatin IQ 160 2.21

Interestingly, image quality scores might not provide a clearindication of a spoofing attempt, because in the initial ninesamples, there was no statistically significant differencebetween the image quality score means ofthe two groups.

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B. Experiment 2Using the Aware software, the minutiae count and qualityscores were computed for all the live, and silicon fingerprintscollected in the second experiment. Table II shows thesummary statistics for image quality and minutiae count forthe dataset of live and silicon fingerprints.

TABLE IISUMMARY STATISTICS

Groups N Median Median MinutiaeImage CountQuality Score

Live Fingers 3600 80.0 39.0Silicon Fingers 3600 69.0 39.0Groups N Mean Image Mean Minutiae

Quality Score CountLive Fingers 3600 76.46 39.44Silicon Fingers 3600 61.35 38.98

Fig. 11 and 12 show the box plot of quality scores andminutiae count respectively. The spread of the image qualityscores is a lot larger for silicone finger compared to livefingers.

80

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Live Finger Silio Finger

Fig. 11 Box plot, image quality scores using Aware image qualitysoftware

100-

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Live Mintae Count Silicone Minutae Count

Fig. 12 Box plot, Minutiae count

A paired t-test test for statistically significant difference wasconducted on image quality scores. The test showed a p-valueof < .05, which indicated that the difference in iimage qualityscores was statistically significant. The minutiae count for thedataset of silicon fingerprints and dataset of live fingerprints isvery similar, but the quality scores for the two groups weresignificantly different. The results from this experimentindicate that image quality scores from the silicon fingerprintsare of medium quality, but they still are significantly differentfrom image quality of live fingers. This provides aninteresting observation - the extraction of minutiae points isvery similar between silicon mold fngerprints and livefingerprint images, but the difference in quality scores pointsto noise in the ridge flow, and contrast levels between ridgesand valleys and other factors. This could also be possible dueto distortion and elastic deformation of the silicon fingerprintbeing different than the corresponding live fingerprint. Thisobservation merits ftuther research into this phenomenon sincethe ridge flow and contrast analysis could be used as adifferentiating factor. The spread of the image quality scoresof the silicon mold is also more variable than the imagesquality scores of the live fmgerprint images, which indicatesdegradation of the silicon fingerprints between successiveattempts.

VI. CONCLUSION

The danger with providing a recipe for spoofing is that anattack methodology to a biometric sensor is revealed.However, in this case, the attack is analogous to an individualrevealing a PIN number to a fraudster and accompanying thefraudster to the ATM to watch the fraudster withdraw theindividual's money. The test was not designed as a spoofingenquiry to evaluate the security of the system, but rather tounderstand the characteristics of the gelatin finger and siliconfmger compared to its live counterpart.Some interesting results were obtained as result of the

analysis in both the experiments. In the first experiment, thegelatin finger was able to provide 163 samples with anacquisition rate of 90.7%. Further analysis of fingerprints fromthe live and gelatin fingers showed a considerable differencein the basic characteristics between the two groups. Repeateduse of the gelatin finger resulted in a rapid degradation of thequality of prints provided, which was reinforced by anincrease in minutiae count with repeated use. Expecting agelatin finger to survive over a 100 attempts would beunreasonable, but our analysis showed that even after 10 uses,the gelatin finger showed a severe degradation in quality, eventhough the system matched the spoof. Stabilization of theminutiae count for the live fingerprint was an unexpectedresult of the experiment, but it reaffins the notion ofhabituation and how it can be used to acquire fingerprintsamples representative of its owner. Comparing the minutiaecount results of the first and second experiment, the minutiaecount of silicon mold fingerprints was more similar to the livefingerprints compared to the gelatin mold fingerprints. Afuture work of this study is to perform matching operationsbetween the live fingerprints and silicon fingerprints toexamine an effect of quality and minutiae count on the

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Boxplot of Live Finger & Silicone Finger Image Quality Scores

Boxplot of Live Finger, Silicone Finger

*

inn-Iiw


matching error rates. Results from both the experimentsshowed a statistically significant difference in image qualityscores between the fake fingerprints and live fingerprintswhich could be used as an anti-spoofing mechanism.Understanding the characteristics of fake fingerprints isimportant in devising countermeasures, and extremelyimportant in increasing security of fingerprint biometricsystems.

REFERENCES

[11 T. Matsumoto, H. Matsumoto, K. Yamada, and S. Hoshino, "Impact ofartificial 'gummy' fingers on fingerprint systems, i Proc. SPIE, vol.4677, Optical Security and Counterfeit Deterrence Techniques IV, SanJose, CA, 2002, pp. 275-289.

2] R. K. Rowe. "A multispectral sensor for fingerprint spoof detection.Sensors, " January 2005, p. 4.

[3] N. K. Ratha, J. H. Connell, and R. M. Bolle, "Enhancing security andprivacy in biometrics-based authentication systems," IBM Syst J 40(3),2001, pp. 614-634.

[4] A. Mansfield and J. Wayman, "Best practices in testing and reportingperfonnances of biometric devices," UK Biometric Working Group,City, ST, 2002, pp. 1-32.

[5] U.S. Department of Homeland Security, Transportation workeridentification credential (TWIC) program, Available online:https://www.twicprogram.com.

[6] National Institute of Standards and Technology, Personal identityverification of federal employees and eontractors, Availabke online:http: /csrc.nist.gov/piv-program.

[7] S. J. Elliott, N. Sickler, and E. Kukula. Automatic identification and datacapture. 3rd ed., West Lafayette, IN: Copymat. 2005, p. 314.

[8] A. Jam and N. Duta. "Deformable matching of hand shapes for userverification," in 1999 Int Conf Image Processing, 1999, Kobe, Japan:IEEE.

[9] N. Haas, S. Pankanti, and M. Yao, "Fingerprint quality assessment"Automatic fingerprint recognition systems, NY: Springer-Verlag, 2004,pp. 55-66.

[10] F. Fernandez, J. Aguilar, and J. Garcia, "A review of schemes forfingerprint image quality computation," in 3rd COST 275 Workshop,Biometrics on the Internet, Hatfield, UK, 2005.

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(2007) Image Quality and Minutiae Count Comparison for Genuine and Artificial Fingerprints

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Transcript of (2007) Image Quality and Minutiae Count Comparison for Genuine and Artificial Fingerprints