(2005) An Evaluation Of Fingerprint Image Quality Across An Elderly Population Vis A Vis An 18 25 Year Old Population

AN EVALUATION OF FINGERPRINT IMAGE QUALITY ACROSS AN ELDERLY POPULATION 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 or behavioral characteristic unique to the user. Therefore, a biometric system requires something that a person "is", This study evaluated fingerprint quality across two and not something that the person knows (secret) or has populations, elderly and young, in order to assess age and (token). The most widely implemented biometric system moisture as potential factors affecting utility image uses fingerprint recognition technology. The volume of quality. Specifically, the examination of these variables use of fingerprint recognition technology can be attributed was conducted on a population over the age of 62, and a to the large number of applications in which it can be population between the ages of 18 and 25, using two used. Applications include: financial services, health fingerprint recognition devices (capacitance and optical). care, electronic commerce, telecommunications, and Collected individual variables included: age, gender, government [2]. ethnic background, handedness, moisture content of each The following are two examples of applications that index finger, occupation(s), subject's use of hand currently use fingerprint recognition devices. Purdue moisturizer, and prior usage of fingerprint devices. Employee Federal Credit Union (PEFCU), in West Computed performance measures included failure to Lafayette, Indiana, integrated ATMs with capacitive- enroll, and quality scores. The results indicated there was based fingerprint recognition sensors in its One Touch statistically significant evidence that both age and program (formerly known as TARAtouch). Users can moisture affected effectiveness image quality of each deposit and withdraw money, and receive account index finger at a=0.01 on the optical device, and there statements after entering their account number, and was statistically significant evidence that age affected presenting the fingerprint used to enroll in the One Touch effectiveness image quality of each index finger on the program. PEFCU's fingerprint ATM enrollees have not capacitance device, but moisture was only significant for experienced a single case of fraudulent use since the the right index finger at a= 0.01. deployment of the biometrically enabled ATMs six years 1. INTRODUCTION ago [3]. Furthermore, Arnold states "individuals over the age of 55 were the most accepting to the idea of gaining Traditional methods of automatic personal identification access to their money without using passwords" [4]. This are based on one, or a combination, of the following two is important to understand, since elderly or retired security measures: a secret or a token. Secret-based individuals generally have more expendable money and security methods require users to provide information that more time to travel. However, the success of a fingerprint only they have knowledge of, such as a password or a biometric system deployed in the public, such as point-of- personal identification number (PIN). Token-based sale or airport identification, would likely fail if the security methods require users to present an item that is in system discriminates against certain populations that are their possession, such as a key, security badge or an prone to have poor fingerprint utility image quality automatic teller machine (ATM) card [1]. Concerns (usefulness of the image, from the system's standpoint), regarding the security of systems using these methods which includes the elderly population. arise from the fact that the system cannot determine if the Eight states (Arizona, California, Connecticut, Illinois, individual providing the secret or the token is, indeed, the Massachusetts, New Jersey, New York, and Texas) have intended user. Tokens can be lost, stolen, and forged, implemented fingerprint technology in the welfare benefit while secrets can be compromised and "surprisingly, 25 programs of some counties. The welfare applicants of percent of people appear to write their PIN on their ATM these counties are required to submit fingerprint samples cards" [2]. More secure methods of automatic personal in order to receive benefits. The purpose of keeping identification receiving attention are biometrics. Unlike fingerprint records of applicants is to "eliminate duplicate secret or token-based systems, a biometric system participation ... deter fraud ... and [restore] the public's provides the security that the approved user interacted confidence in the integrity of the welfare system [5]." with the system, by matching or not matching a physical 0-7803-9245-0/05/$20.00 C2005 IEEE Authorized licensed use limited to: Purdue University. Downloaded on December 14, 2009 at 17:31 from IEEE Xplore. Restrictions apply.

Here again, the integrity of a fingerprint system would be to manual labor, injuries, disease, scars or other reduced if the system failed to enroll, verify, or identify an circumstances [10] such as loose or wrinkled skin. These individual due to poor fingerprint utility image quality. contact issues may introduce false minutiae points into the captured image, causing higher FTE, FTA, FMR, and 2. QUALITY AND USAGE ISSUES WITH THE FNMR. Both contact issues can be observed in Figure 1, ELDERLY in comparison to a normal image (Figure 2). The general biometric model can be applied to the fingerprint recognition process; likewise, potential problem areas of a fingerprint system can be paralleled to areas of the general biometric model. When placed into the general biometric model, two areas of the fingerprint recognition system (data collection and signal processing) are affected by the failure of interaction between the system and the user: non-uniform contact and irreproducible contact. The problem of interaction between the user and the system affects the sub-category of presentation within the data collection silo. If the user Figure 1: Dry, worn fingerprint (left) and resulting cannot present a fingerprint to the device, then enrollment minutiae points (right). (and subsequent verification or identification attempts) is not possible through fingerprint recognition. Irreproducible and non-uniform contacts affect the sub- categories of feature extraction and quality control within the signal processing silo. Non-uniform contact tends to produce images of low quality, resulting in poor feature extraction of the presented fingerprint, whereas irreproducible contact may allow for images with high quality 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 of the most important aspects for a biometric system, as it is Figure 2: Normal fingerprint (left) and resulting minutiae this quality parameter that determines whether a captured points (right). image is acceptable for further use within the biometric system. The utility quality of a presented fingerprint The resulting utility quality scores of the fingerprint image is developed and processed by the quality control images in Figures 1 and 2 are displayed in Figures 3 and function of the biometric system, and a score, based on 4, respectively. the image's usability, is assigned to that image. It is these quality scores and captured images that provide the data used by the biometric system and allow it to make an accept/reject decision. Discussion of poor image quality issues of elderly fingerprints occurs in the biometric literature [2], [6], [7], [8], [9]. These issues pose problems for fingerprint recognition systems during the enrollment, verification, and identification processes. Therefore, the objective of this study was to determine the impact that particular variables, namely age and moisture, had on the utility quality of fingerprint images. Two of the most common causes of poor utility quality are attributable to non-uniform and irreproducible contact (Figure 1), which occur between the fingerprint and the platen of a fingerprint sensor. Non-uniform contact can result when the presented fingerprint is too dry or too wet, Figure 3: Image quality calculation using commercially and irreproducible contact occurs when the fingerprint available software on a dry, worn fingerprint (low utility ridges are semi-permanently or permanently changed due quality). Authorized licensed use limited to: Purdue University. Downloaded on December 14, 2009 at 17:31 from IEEE Xplore. Restrictions apply.

ridges. The ridge charges and valley non-charges are converted to pixel values, resulting in a fingerprint image [12]. The device has an additional feature, which properly aligns the subject's finger in order to capture the most distinctive area of the fingerprint. This "ridge lock" is situated at the base of the device's platen and fits into the groove of the joint closest to the fingernail of the presented finger. The capacitance sensor has a 13 mm x 13 mm platen chip grid, and produces an image of 250 dots per inch (dpi) from the converted charges. The optical sensor acquires an image using a charged couple device (CCD) camera, light emitting diode (LED) illumination, and a prism. When a finger is placed on the platen, which is one side of the prism, the CCD camera captures an image of the signal reflected by the fingerprint [12]. The optical sensor has an image acquisition surface Figure 4: Image quality calculation using commercially of 13 mm x 18 mm and produces an image of 500 dpi available software on a normal fingerprint image (high from the reflected signal. utility quality). 3.2. Moisture Checker Furthermore, the causes of these two contact issues have a The device selected for measuring the moisture content of higher likeliness of occurring when an elderly user the fingerprint region was Scalar America's MY707S skin presents the enrolled fingerprint to the fingerprint device moisture checker. This device obtains moisture readings [10]. As individuals age, their skin becomes drier, sags using electrical conductivity and has a reading accuracy of from the loss of collagen and elastin fibers, becomes +/- 0.2 percent. Approximately 80 percent of the moisture thinner and loses fat; all of these conditions decrease the reading is influenced by the top ten microns of the skin, firmness of the skin, causing wrinkles [11]. Skin and approximately 90 precent by the top 200 microns exhibiting these symptoms is likely to have incurred semi- [13]. permanent or permanent damage over the life of the individual. 3.3. Computer Hardware Another issue involves some elderly individuals being A DellTm Dimension.TM workstation served as the platform unable to properly present the enrolled fingerprint to the to communicate with the optical sensor and had the fingerprint device. An elderly individual's ability to use a following specifications: 2 GHz Intel® Pentium® 4 fingerprint device can be severely limited by arthritis or a processor; 512 MB of 2100 double data rate memory; 40- loss of motor skills, which may affect the quality of GB, 7200-rpm hard drive; and Microsoftg Windowsg captured images. 2000 operating system with Service Pack 2. The workstation was loaded with Neurotechnologija's 3. PROCEDURES VeriFinger 4.1 software, the drivers for the optical sensor, and was used to store the images captured from the The purpose of this study was to evaluate the fingerprint sensor. A demographic survey and instructions for quality of an elderly population and compare it to an 18- interacting with each device were also presented to each to 25-year-old population, in order to assess potential participate on this workstation using Microsoftg factors affecting utility image quality. This section PowerPoint®'97, a headset with adjustable volume, and a describes: the software and hardware, the variables 17-inch LCD monitor. examined, the type of study, and the test procedures A DellTM InspironTm 8200 laptop computer served as the undertaken to evaluate fingerprint quality. platform to communicate with the capacitance sensor and had the following specifications: 1.8 GHz Mobile Intelg 3.1. Sensors Pentium® 4 processor, 256 MB of 2100 double data rate The two fingerprint recognition sensors used in this study memory; 40-GB, 4200-rpm hard drive; and Microsoft® included a capacitance sensor, and an optical sensor. The Windowsg XP Home Edition operating system. The capacitance sensor acquires an image using electrical laptop was loaded with image capturing software, and the charges. When a finger is placed on the capacitive chip drivers for the capacitance sensor; the laptop stored the grid (platen), electrical charges accumulate at the points images captured from the capacitance sensor. where the finger ridges contact the chip grid. Absence of an electrical charge indicates a valley between the finger 3.4. Selected Features to be Recorded Authorized licensed use limited to: Purdue University. Downloaded on December 14, 2009 at 17:31 from IEEE Xplore. Restrictions apply.

Independent variables included age, and moisture content the utility quality was established and incorporated with of each index finger. The dependent variable was the the previously collected demographic and moisture data. utility quality of the fingerprint images derived from The data were then analyzed using the GLM function of Aware, Inc.'s Fingerprint Image Quality API. SAS® 8e. 3.5. Evaluation Classification 4. RESULTS This study is best described as a scenario evaluation (Table 1). According to the UK Biometrics Working The study examined two hypotheses. The first hypothesis Group Best Practice Document 2.01 [14], the goal of a stated that there is no statistically significant difference in scenario test "is to determine the performance of a the fingerprint quality between the age groups 18-25 and complete biometric system in a specific application 62+. The second hypothesis stated that there is no environment with a specific target population." statistically significant difference between the fingerprint This study satisfies this statement, since two complete moisture content of the age groups 18-25 and 62+. Two biometric systems are being tested in conditions similar to population age groups were targeted, the elderly (62+) some e-commerce, ATM banking, and point-of-sale and a younger (18-25 years). No subject was excluded environments using specific populations (18- to 25-year- from participating based on age, but data from subjects olds and 62 years and older). not falling into one of these age groups were excluded in the analysis. The minimum age was set to 18 years old, Table 1. Scenario Evaluation Criteria since individuals this age and older are considered adults and do not need a guardian's consent to participate. The Application Experiment maximum age of the younger population was set to 25 Classification years old, in order to establish the typical age range for System classification Positive Identification college or university students. The recruitment of the 18- Cooperative versus Cooperative to 25-year-old population was conducted in the School of Non-cooperative Technology Department of Industrial Technology, which Overt versus Covert Overt has a higher percentage of white males than minority Habituated versus Non- Variable males, white females, or minority females. Consequently, habituated there was a higher rate of white males among the subjects. Attended versus Non- Attended Participation for the 62+ age group was open to all attended individual's partaking in activities at Purdue University's Ismail Center and residents of Westminster Village. The Standard Environment Lab environment, room Ismail Center had an approximately equal number of lighting, temperature males and females, with most members being of a Public versus Private N/A Caucasian/white ethnic background. Westminster Village Open versus Closed Closed had approximately three times as many females as males, with nearly 100 percent of the residents being 3.6. Compliance with Best Practices Caucasian/white. Therefore, a higher rate of Caucasian This study conformed to recommendations established by females than minority females participated in this study. the UK Biometrics Working Group Best Practice Document 2.01. 4.1. Hypothesis 1 For Hypothesis 1, a one-way analysis of variance 3.7. Test Procedures (ANOVA) computation using the GLM function was After the volunteers consented to participate, they were conducted in order to examine the statement that shown a Microsoft® PowerPoint® presentation fingerprint image utility quality is not affected by age. describing the proper interaction with each fingerprint The computation for this one-way ANOVA (Table 2) device. This presentation was followed by a short survey included data from the capacitance and the optical sensors used to collect demographic information. There were a for each index finger. total of four sessions for this study, one enrollment and three verification; each session was separated by Table 2. Image quality and age ANOVA approximately one week. The order of the device and Right Index Left Index index finger used was randomized for each session, with Capacitance F value = 100.16 F value of 116.75 the moisture content being measured before each attempt. p value <.0001* p value <.0001* Captured images were automatically named using code 39 bar codes encoded with the appropriate identifiers for Optical F value = 180.44 F value = 203.89 each participant and then saved. After image collection, p value <.0001* p value <.0001* Authorized licensed use limited to: Purdue University. Downloaded on December 14, 2009 at 17:31 from IEEE Xplore. Restrictions apply.

* Significant at oc = 0.01 response to age (Figure 6). The graph in Figure 6 illustrates this correlation. The results of the ANOVA computed for the utility image quality and age suggested that there was indeed a MDI STURE 101 + +F: correlation between image quality and age, regardless of which device or index finger was examined. The Pearson correlation (r = -.78) was also calculated for the image utility quality in response to age (Figure 5). The graph in i.. Figure 5 shows that a linear correlation exists. Ht+ +'t 'X i ~~~~~~~~~~~~~~~+ Z9 X '49 0 P)l n to C.) ACE Figure 6: Graphical plot of the relationship between moisture content and age. Based upon these findings, it was concluded that the moisture content data were statistically significant at oc 20 X 40 5 6 Go 4 to s 9a |§ = 0.01 for each index finger using the optical sensor. The AGE Figure 5: Graphical plot of the correlation between moisture content data were also statistically significant for image quality and age. the right index finger using the capacitance sensor, but were not statistically significant for the left index finger. Based upon these findings, it was concluded that the Therefore, Hypothesis 2 is rejected at oc = 0.01 for each image utility quality data were statistically significant at oc index finger using the optical sensor and for the right = 0.01 for each index finger, as well as for each sensor index finger using the capacitance sensor. when tested against age. Therefore, Hypothesis 1 is 5. CONCLUSION rejected at oc = 0.01. The purpose of this study was to evaluate the fingerprint 3.1. Hypothesis 2 utility image quality of an elderly population in For Hypothesis 2, a one-way ANOVA computation (using comparison to an 18- to 25-year-old population baseline. the SAS GLM function) was conducted in order to During the formulation of this study, two hypotheses were examine the statement that fingerprint moisture content is generated and examined after the collection and analysis not affected by age. The computation for this one-way of the data. The first hypothesis states that there is no ANOVA (Table 3) included data from the capacitance and statistically significant difference in the fingerprint image the optical sensors for each index finger. utility quality between the age groups 18-25 and 62+. This Table 3. Moisture content and age ANOVA hypothesis was rejected at oc = 0.01 for each fingerprint device (capacitance sensor and optical sensor), regardless Right Index Left Index of the index finger used. The second hypothesis states that Capacitance F value = 9.10 F value of 2.93 there is no statistically significant difference between the p value <.0032* p value <.09 fingerprint moisture content of the age groups 18-25 and Optical F value = 18.22 F value = 10.13 62+. This hypothesis was rejected at oc = 0.01 for both p value <.0001* p value <.0019* index fingers when used with the optical sensor, and it * Significant at oc = 0.01 was rejected for the right index finger in conjunction with the capacitance sensor. However, this hypothesis failed to The results of the ANOVA computed for the moisture be rejected at oc = 0.01 for the left index finger and the content and age suggested that there was, in part, a capacitance sensor. Other observations made throughout correlation between moisture and age, albeit not as strong the study were briefly examined, but only with anecdotal as image quality vs. age. The Pearson correlation (r= - data. .38) was also calculated for the moisture content in Authorized licensed use limited to: Purdue University. Downloaded on December 14, 2009 at 17:31 from IEEE Xplore. Restrictions apply.

in Testing and Reporting of Biometric Devices 2.01," September 2002, w . 6. REFERENCES /ssf [1] P. Meenan and R. Adhami, "Fingerprinting For Security," IEEE Potentials, 2002, 33-38. [2] A. Jain, L. Hong, and S. Pankanti, "Biometrics: Promising frontiers for emerging identification market," Comm. ACM, 91-98, February 2000. [3] L. Mearian, "Want Access? Give'em the Finger," November 2002, [4] B. Arnold (private communication), 2002. [5] D. Burton, R. Salstrom, & J. L. Wayman, "A Proposed Cost/Benefit Analysis of Finger Imaging Systems in Social Service Applications," presented at the 12th Annual CSU-POM Conference, California State University, Sacramento, 2000. [6] G. Behrens, "Assessing the Stability Problems of Biometric Features," presented at the International Biometrics 2002, Amsterdam, 2002. [7] D. J. Buettner, "A Large-Scale Biometric Identification System at the Point of Sale," September 2002, -2 Final%0dL0Ou,tnr2Bi f [8] A. K. Jain and S. Pankanti, Advances in Fingerprint Technology (2nd ed.). New York: Elsevier, 2001. [9] X. Jiang and W. Ser, "Online Fingerprint Template Improvement," IEEE Trans. Pattern Analysis and Machine Intelligence vol. 24(8), pp. 1121-1126, 2002. [10] A. Jain, L. Hong, and R. Bolle, "On-Line Fingerprint Verification," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19(4), pp. 302-314, 1997. [11] American Academy of Dermatology, "Mature Skin," November 2002, http llwww.aa [12] N. K. Ratha, A. Senior, and R. M. Bolle, "Automated Biometrics," Proceedings of ICAPR-2001, 2001. [13] Scalar America, "MY707S Moisture Checker White Papers," Scalar America Documentation, 2003 [14] A. J. Mansfield, and J. L. Wayman, "Best Practices Authorized licensed use limited to: Purdue University. Downloaded on December 14, 2009 at 17:31 from IEEE Xplore. Restrictions apply.

(2005) An Evaluation Of Fingerprint Image Quality Across An Elderly Population Vis A Vis An 18 25 Year Old Population

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(2005) An Evaluation Of Fingerprint Image Quality Across An Elderly Population Vis A Vis An 18 25 Year Old Population — Document Transcript