AN EVALUATION OF FINGERPRINT IMAGE QUALITY ACROSS AN ELDERLYPOPULATION VIS-A-VIS AN 18-25 YEAR OLD POPULATION

Nathan C. Sickler & Stephen J Elliott, PhD

Purdue University, College of Technology, Department of Industrial Technology

ABSTRACT

This study evaluated fingerprint quality across twopopulations, elderly and young, in order to assess age andmoisture as potential factors affecting utility imagequality. Specifically, the examination of these variableswas conducted on a population over the age of 62, and apopulation between the ages of 18 and 25, using twofingerprint recognition devices (capacitance and optical).Collected individual variables included: age, gender,ethnic background, handedness, moisture content of eachindex finger, occupation(s), subject's use of handmoisturizer, and prior usage of fingerprint devices.Computed performance measures included failure toenroll, and quality scores. The results indicated there wasstatistically significant evidence that both age andmoisture affected effectiveness image quality of eachindex finger at a=0.01 on the optical device, and therewas statistically significant evidence that age affectedeffectiveness image quality of each index finger on thecapacitance device, but moisture was only significant forthe right index finger at a= 0.01.

1. INTRODUCTION

Traditional methods of automatic personal identificationare based on one, or a combination, of the following twosecurity measures: a secret or a token. Secret-basedsecurity methods require users to provide information thatonly they have knowledge of, such as a password or apersonal identification number (PIN). Token-basedsecurity methods require users to present an item that is intheir possession, such as a key, security badge or anautomatic teller machine (ATM) card [1]. Concernsregarding the security of systems using these methodsarise from the fact that the system cannot determine if theindividual providing the secret or the token is, indeed, theintended user. Tokens can be lost, stolen, and forged,while secrets can be compromised and "surprisingly, 25percent of people appear to write their PIN on their ATMcards" [2]. More secure methods of automatic personalidentification receiving attention are biometrics. Unlikesecret or token-based systems, a biometric systemprovides the security that the approved user interactedwith the system, by matching or not matching a physical

or behavioral characteristic unique to the user. Therefore,a biometric system requires something that a person "is",and not something that the person knows (secret) or has(token). The most widely implemented biometric systemuses fingerprint recognition technology. The volume ofuse of fingerprint recognition technology can be attributedto the large number of applications in which it can beused. Applications include: financial services, healthcare, electronic commerce, telecommunications, andgovernment [2].The following are two examples of applications thatcurrently use fingerprint recognition devices. PurdueEmployee Federal Credit Union (PEFCU), in WestLafayette, Indiana, integrated ATMs with capacitive-based fingerprint recognition sensors in its One Touchprogram (formerly known as TARAtouch). Users candeposit and withdraw money, and receive accountstatements after entering their account number, andpresenting the fingerprint used to enroll in the One Touchprogram. PEFCU's fingerprint ATM enrollees have notexperienced a single case of fraudulent use since thedeployment of the biometrically enabled ATMs six yearsago [3]. Furthermore, Arnold states "individuals over theage of 55 were the most accepting to the idea of gainingaccess to their money without using passwords" [4]. Thisis important to understand, since elderly or retiredindividuals generally have more expendable money andmore time to travel. However, the success of a fingerprintbiometric system deployed in the public, such as point-of-sale or airport identification, would likely fail if thesystem discriminates against certain populations that areprone to have poor fingerprint utility image quality(usefulness of the image, from the system's standpoint),which includes the elderly population.Eight states (Arizona, California, Connecticut, Illinois,Massachusetts, New Jersey, New York, and Texas) haveimplemented fingerprint technology in the welfare benefitprograms of some counties. The welfare applicants ofthese counties are required to submit fingerprint samplesin order to receive benefits. The purpose of keepingfingerprint records of applicants is to "eliminate duplicateparticipation ... deter fraud ... and [restore] the public'sconfidence in the integrity of the welfare system [5]."

0-7803-9245-0/05/$20.00 C2005 IEEE

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Here again, the integrity of a fingerprint system would bereduced if the system failed to enroll, verify, or identify anindividual due to poor fingerprint utility image quality.

2. QUALITY AND USAGE ISSUES WITH THEELDERLY

The general biometric model can be applied to thefingerprint recognition process; likewise, potentialproblem areas of a fingerprint system can be paralleled toareas of the general biometric model. When placed intothe general biometric model, two areas of the fingerprintrecognition system (data collection and signal processing)are affected by the failure of interaction between thesystem and the user: non-uniform contact andirreproducible contact. The problem of interactionbetween the user and the system affects the sub-categoryof presentation within the data collection silo. If the usercannot present a fingerprint to the device, then enrollment(and subsequent verification or identification attempts) isnot possible through fingerprint recognition.Irreproducible and non-uniform contacts affect the sub-categories of feature extraction and quality control withinthe signal processing silo. Non-uniform contact tends toproduce images of low quality, resulting in poor featureextraction of the presented fingerprint, whereasirreproducible contact may allow for images with highquality but, depending on the severity of the contact issue,image quality can be adversely affected.Moreover, the utility quality of a captured image is one ofthe most important aspects for a biometric system, as it isthis quality parameter that determines whether a capturedimage is acceptable for further use within the biometricsystem. The utility quality of a presented fingerprintimage is developed and processed by the quality controlfunction of the biometric system, and a score, based onthe image's usability, is assigned to that image. It is thesequality scores and captured images that provide the dataused by the biometric system and allow it to make anaccept/reject decision.Discussion of poor image quality issues of elderlyfingerprints occurs in the biometric literature [2], [6], [7],[8], [9]. These issues pose problems for fingerprintrecognition systems during the enrollment, verification,and identification processes. Therefore, the objective ofthis study was to determine the impact that particularvariables, namely age and moisture, had on the utilityquality of fingerprint images.Two of the most common causes of poor utility qualityare attributable to non-uniform and irreproducible contact(Figure 1), which occur between the fingerprint and theplaten of a fingerprint sensor. Non-uniform contact canresult when the presented fingerprint is too dry or too wet,and irreproducible contact occurs when the fingerprintridges are semi-permanently or permanently changed due

to manual labor, injuries, disease, scars or othercircumstances [10] such as loose or wrinkled skin. Thesecontact issues may introduce false minutiae points into thecaptured image, causing higher FTE, FTA, FMR, andFNMR. Both contact issues can be observed in Figure 1,in comparison to a normal image (Figure 2).

Figure 1: Dry, worn fingerprint (left) and resultingminutiae points (right).

Figure 2: Normal fingerprint (left) and resulting minutiaepoints (right).

The resulting utility quality scores of the fingerprintimages in Figures 1 and 2 are displayed in Figures 3 and4, respectively.

Figure 3: Image quality calculation using commerciallyavailable software on a dry, worn fingerprint (low utilityquality).

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Figure 4: Image quality calculation using commerciallyavailable software on a normal fingerprint image (highutility quality).

Furthermore, the causes of these two contact issues have ahigher likeliness of occurring when an elderly userpresents the enrolled fingerprint to the fingerprint device[10]. As individuals age, their skin becomes drier, sagsfrom the loss of collagen and elastin fibers, becomesthinner and loses fat; all of these conditions decrease thefirmness of the skin, causing wrinkles [11]. Skinexhibiting these symptoms is likely to have incurred semi-permanent or permanent damage over the life of theindividual.Another issue involves some elderly individuals beingunable to properly present the enrolled fingerprint to thefingerprint device. An elderly individual's ability to use afingerprint device can be severely limited by arthritis or aloss of motor skills, which may affect the quality ofcaptured images.

3. PROCEDURES

The purpose of this study was to evaluate the fingerprintquality of an elderly population and compare it to an 18-to 25-year-old population, in order to assess potentialfactors affecting utility image quality. This sectiondescribes: the software and hardware, the variablesexamined, the type of study, and the test proceduresundertaken to evaluate fingerprint quality.

3.1. SensorsThe two fingerprint recognition sensors used in this studyincluded a capacitance sensor, and an optical sensor. Thecapacitance sensor acquires an image using electricalcharges. When a finger is placed on the capacitive chipgrid (platen), electrical charges accumulate at the pointswhere the finger ridges contact the chip grid. Absence ofan electrical charge indicates a valley between the finger

ridges. The ridge charges and valley non-charges areconverted to pixel values, resulting in a fingerprint image[12]. The device has an additional feature, which properlyaligns the subject's finger in order to capture the mostdistinctive area of the fingerprint. This "ridge lock" issituated at the base of the device's platen and fits into thegroove of the joint closest to the fingernail of thepresented finger. The capacitance sensor has a 13 mm x13 mm platen chip grid, and produces an image of 250dots per inch (dpi) from the converted charges.The optical sensor acquires an image using a chargedcouple device (CCD) camera, light emitting diode (LED)illumination, and a prism. When a finger is placed on theplaten, which is one side of the prism, the CCD cameracaptures an image of the signal reflected by the fingerprint[12]. The optical sensor has an image acquisition surfaceof 13 mm x 18 mm and produces an image of 500 dpifrom the reflected signal.

3.2. Moisture CheckerThe device selected for measuring the moisture content ofthe fingerprint region was Scalar America's MY707S skinmoisture checker. This device obtains moisture readingsusing electrical conductivity and has a reading accuracy of+/- 0.2 percent. Approximately 80 percent of the moisturereading is influenced by the top ten microns of the skin,and approximately 90 precent by the top 200 microns[13].

3.3. Computer HardwareA DellTm Dimension.TM workstation served as the platformto communicate with the optical sensor and had thefollowing specifications: 2 GHz Intel® Pentium® 4processor; 512 MB of 2100 double data rate memory; 40-GB, 7200-rpm hard drive; and Microsoftg Windowsg2000 operating system with Service Pack 2. Theworkstation was loaded with Neurotechnologija'sVeriFinger 4.1 software, the drivers for the optical sensor,and was used to store the images captured from thesensor. A demographic survey and instructions forinteracting with each device were also presented to eachparticipate on this workstation using MicrosoftgPowerPoint®'97, a headset with adjustable volume, and a17-inch LCD monitor.A DellTM InspironTm 8200 laptop computer served as theplatform to communicate with the capacitance sensor andhad the following specifications: 1.8 GHz Mobile IntelgPentium® 4 processor, 256 MB of 2100 double data ratememory; 40-GB, 4200-rpm hard drive; and Microsoft®Windowsg XP Home Edition operating system. Thelaptop was loaded with image capturing software, and thedrivers for the capacitance sensor; the laptop stored theimages captured from the capacitance sensor.

3.4. Selected Features to be Recorded

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Independent variables included age, and moisture contentof each index finger. The dependent variable was theutility quality of the fingerprint images derived fromAware, Inc.'s Fingerprint Image Quality API.

3.5. Evaluation ClassificationThis study is best described as a scenario evaluation(Table 1). According to the UK Biometrics WorkingGroup Best Practice Document 2.01 [14], the goal of a

scenario test "is to determine the performance of a

complete biometric system in a specific applicationenvironment with a specific target population."This study satisfies this statement, since two completebiometric systems are being tested in conditions similar tosome e-commerce, ATM banking, and point-of-saleenvironments using specific populations (18- to 25-year-olds and 62 years and older).

Table 1. Scenario Evaluation Criteria

Application ExperimentClassificationSystem classification Positive IdentificationCooperative versus CooperativeNon-cooperativeOvert versus Covert OvertHabituated versus Non- VariablehabituatedAttended versus Non- AttendedattendedStandard Environment Lab environment, room

lighting, temperaturePublic versus Private N/AOpen versus Closed Closed

3.6. Compliance with Best PracticesThis study conformed to recommendations established bythe UK Biometrics Working Group Best PracticeDocument 2.01.

3.7. Test ProceduresAfter the volunteers consented to participate, they were

shown a Microsoft® PowerPoint® presentationdescribing the proper interaction with each fingerprintdevice. This presentation was followed by a short survey

used to collect demographic information. There were a

total of four sessions for this study, one enrollment andthree verification; each session was separated byapproximately one week. The order of the device andindex finger used was randomized for each session, withthe moisture content being measured before each attempt.Captured images were automatically named using code 39bar codes encoded with the appropriate identifiers foreach participant and then saved. After image collection,

the utility quality was established and incorporated withthe previously collected demographic and moisture data.The data were then analyzed using the GLM function ofSAS® 8e.

4. RESULTS

The study examined two hypotheses. The first hypothesisstated that there is no statistically significant difference inthe fingerprint quality between the age groups 18-25 and62+. The second hypothesis stated that there is nostatistically significant difference between the fingerprintmoisture content of the age groups 18-25 and 62+. Twopopulation age groups were targeted, the elderly (62+)and a younger (18-25 years). No subject was excludedfrom participating based on age, but data from subjectsnot falling into one of these age groups were excluded inthe analysis. The minimum age was set to 18 years old,since individuals this age and older are considered adultsand do not need a guardian's consent to participate. Themaximum age of the younger population was set to 25years old, in order to establish the typical age range forcollege or university students. The recruitment of the 18-to 25-year-old population was conducted in the School ofTechnology Department of Industrial Technology, whichhas a higher percentage of white males than minoritymales, white females, or minority females. Consequently,there was a higher rate of white males among the subjects.Participation for the 62+ age group was open to allindividual's partaking in activities at Purdue University'sIsmail Center and residents of Westminster Village. TheIsmail Center had an approximately equal number ofmales and females, with most members being of aCaucasian/white ethnic background. Westminster Villagehad approximately three times as many females as males,with nearly 100 percent of the residents beingCaucasian/white. Therefore, a higher rate of Caucasianfemales than minority females participated in this study.

4.1. Hypothesis 1For Hypothesis 1, a one-way analysis of variance(ANOVA) computation using the GLM function wasconducted in order to examine the statement thatfingerprint image utility quality is not affected by age.The computation for this one-way ANOVA (Table 2)included data from the capacitance and the optical sensorsfor each index finger.

Table 2. Image quality and age ANOVARight Index Left Index

Capacitance F value = 100.16 F value of 116.75p value <.0001* p value <.0001*

Optical F value = 180.44 F value = 203.89p value <.0001* p value <.0001*

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* Significant at oc = 0.01

The results of the ANOVA computed for the utility imagequality and age suggested that there was indeed acorrelation between image quality and age, regardless ofwhich device or index finger was examined. The Pearsoncorrelation (r = -.78) was also calculated for the imageutility quality in response to age (Figure 5). The graph inFigure 5 shows that a linear correlation exists.

Ht+ +'t'X i

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response to age (Figure 6). The graph in Figure 6illustrates this correlation.

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Figure 6: Graphical plot of the relationship betweenmoisture content and age.

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AGE

Figure 5: Graphical plot of the correlation betweenimage quality and age.

Based upon these findings, it was concluded that theimage utility quality data were statistically significant at oc= 0.01 for each index finger, as well as for each sensorwhen tested against age. Therefore, Hypothesis 1 isrejected at oc = 0.01.

3.1. Hypothesis 2For Hypothesis 2, a one-way ANOVA computation (usingthe SAS GLM function) was conducted in order toexamine the statement that fingerprint moisture content isnot affected by age. The computation for this one-wayANOVA (Table 3) included data from the capacitance andthe optical sensors for each index finger.

Table 3. Moisture content and age ANOVARight Index Left Index

Capacitance F value = 9.10 F value of 2.93p value <.0032* p value <.09

Optical F value = 18.22 F value = 10.13p value <.0001* p value <.0019*

* Significant at oc = 0.01

The results of the ANOVA computed for the moisturecontent and age suggested that there was, in part, a

correlation between moisture and age, albeit not as strongas image quality vs. age. The Pearson correlation (r= -

.38) was also calculated for the moisture content in

Based upon these findings, it was concluded thatthe moisture content data were statistically significant at oc= 0.01 for each index finger using the optical sensor. Themoisture content data were also statistically significant forthe right index finger using the capacitance sensor, butwere not statistically significant for the left index finger.Therefore, Hypothesis 2 is rejected at oc = 0.01 for eachindex finger using the optical sensor and for the rightindex finger using the capacitance sensor.

5. CONCLUSION

The purpose of this study was to evaluate the fingerprintutility image quality of an elderly population incomparison to an 18- to 25-year-old population baseline.During the formulation of this study, two hypotheses weregenerated and examined after the collection and analysisof the data. The first hypothesis states that there is nostatistically significant difference in the fingerprint imageutility quality between the age groups 18-25 and 62+. Thishypothesis was rejected at oc = 0.01 for each fingerprintdevice (capacitance sensor and optical sensor), regardlessof the index finger used. The second hypothesis states thatthere is no statistically significant difference between thefingerprint moisture content of the age groups 18-25 and62+. This hypothesis was rejected at oc = 0.01 for bothindex fingers when used with the optical sensor, and itwas rejected for the right index finger in conjunction withthe capacitance sensor. However, this hypothesis failed tobe rejected at oc = 0.01 for the left index finger and thecapacitance sensor. Other observations made throughoutthe study were briefly examined, but only with anecdotaldata.

i..

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in Testing and Reporting of Biometric Devices 2.01,"September 2002, w .

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-2 Final%0dfL0Ou,tnr2Bi

[8] A. K. Jain and S. Pankanti, Advances in FingerprintTechnology (2nd ed.). New York: Elsevier, 2001.

[9] X. Jiang and W. Ser, "Online Fingerprint TemplateImprovement," IEEE Trans. Pattern Analysis andMachine Intelligence vol. 24(8), pp. 1121-1126, 2002.

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[11] American Academy of Dermatology, "Mature Skin,"November 2002, http llwww.aa

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