• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
(2007) Image Quality and Minutiae Count Comparison for Genuine and Artificial Fingerprints
 

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

on

  • 1,023 views

The vulnerabilities of biometric sensors have been...

The vulnerabilities of biometric sensors have been
discussed extensively in the literature and popularized in films and
television shows. This research examines the image quality of an
artificial print as compared to a genuine finger, and examines the
characteristics of the two, including minutiae counts and image
quality, as repeated samples are taken.

Statistics

Views

Total Views
1,023
Views on SlideShare
1,023
Embed Views
0

Actions

Likes
0
Downloads
15
Comments
0

0 Embeds 0

No embeds

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

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

    • 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, and Hale Kim Ph.D. same user after acquisition on a commercially available Abstract-The vulnerabilities of biometric sensors have been biometric fingerprint sensor. This research seeks to answer discussed extensively in the literature and popularized in films and whether the samples (live versus gelatin) exhibit the same television shows. This research examines the image quality of an minutiae counts and whether the acquired images possess the artificial print as compared to a genuine finger, and examines the same image quality properties. That is, are the live and gelatin characteristics of the two, including minutiae counts and image quality, as repeated samples are taken. samples similar in their characteristics, and do their similarities or differences have an impact on matching Keywords-authentication, biometrics, fingerprint recognition, perfonnance? repeatability. II. BIOMETRIC SYSTEM VULNERABILITIES I. INTRODUCTION AND MOTIVATION All security measures, including mechanisms for V ERIFYING the identity of an individual can be authenticating identity, can be circumvented. The processes Vaccomplished by three main considerations: what an associated with working around these measures vary in individual has, what an individual knows or owns, and what difficulty according to the level of effort and resources needed an individual is. The first option is typically achieved through to cany out the deceptive act. Authentication mechanisms the use of a token, such as an identification card, badge, based on secrets are particularly vulnerable to "guessing" magnetic stripe, or radio frequency identification (RFID) tag. attacks. Token mechanisms that rely on the possession of an The second option can be achieved through the use of a object, typically a card or badge technology are most password or personal identification number (PIN). The third vulnerable to theft or falsified reproduction. Biometric option can be accomplished through what an individual is by technologies closely tie the authenticator to the individual utilizing biometric technologies. Like the first two identity of the user through the use of physiological or authentication methods, biometric systems contain behavioral characteristics. While this property offers an added vulnerabilities and are susceptible to attack. Some of these advantage over the other two authentication mechanisms, it vulnerabilities are similar or even overlapping across all three places a great emphasis on validating the integrity of the authentication mechanisms. However, attacks specific to biometric sample acquired and transferred in the biometric biometric systems focus on liveness detection of a human (i.e., system. Ratha, Connell, and Bolle provided a model whether the finger from a live sample or a gelatin sample). identifying vulnerabilities in biometric systems [3]. An Various attacks documented in the literature have focused on example of the threat model is shown in Fig. 1, and builds on the sensor [11, [2]. While understanding and preventing the general biometric model outlined in Mansfield and attacks on the sensor is an interesting research topic worthy of Wayman [4]. investigation, this paper examines the global and local features of a live sample compared to that of a gelatin finger from the S. J. Elliott, Ph.D. is an Associate Professor with the Departnent of Industrial Technology at Purdue University, West Lafayette, IN 47907 USA; (e-mail:elliott apurdue.edu . S. Modi is graduate student with the Departnent of Industrial Technology at Purdue University, West Lafayette, IN 47907 USA; (e-mail:shimon@ purdue.edu). L. Maccarone is an undergraduate student at Purdue University, West Lafayette, IN 47907 USA Fig. 1 Biometric system threat model M. R. Young is a graduate student with the Department of Industrial Technology at Purdue University, West Lafayette, IN 47907 USA C. Jin is a graduate student with the School of Information and The biometric system threat model shown in Fig. 1 contains Communication Engineering at Inha University, Incheon, Korea; (e- 11 individual areas of vulnerability. In addition to the five mail:cljin@vision.inha.ac.kr). main internal modules characterized in the general biometric H. Kim is a Professor with the School of Infornation and Communication Engineering at Inha University, Incheon, Korea; (e-mail:hikim@inha.ac.kr). model (data collection, signal processing, matching, storage, 1-4244-1129-7/07/$25.00 ©2007 IEEE 30 Authorized licensed use limited to: Purdue University. Downloaded on February 18,2010 at 15:00:20 EST from IEEE Xplore. Restrictions apply.
    • and decision) [4], another component is added to represent the the features of the two fingerprints remain consistent transfer of the authentication decision to the application that Repeatability of the extracted features is important for the relies on the decision from the, biometric system. Such matching process in any type of biometric technology [7]. The applications could be identity management systems (IDMS) or features to be examined include: minutiae points and image access control systems for logical and or physical access to quality. One of the challenges associated with this research is resources. These systems can vary in complexity and size, to ensure the image is of sufficient quality. A wide variety of ranging from a local computer log-in all the way to a wide- factors can influence the quality of fingerprint samples. Non- scale distributed architecture seen in the cases of the U.S. uniform contact, inconsistent contact, or irreproducible contact Department of Transportation's Transportation Worker with the fingerprint sensor can result in images with a low Identification Credential (TWIC) [5] or the Personal Identity signal-to-noise ratio, which is not desirable for feature Verification (PIV) of Federal Employees and Contractors [6]. extraction and matching purposes [9]. Wear and tear of the The remaining points of vulnerability are communication skin and aging effects can semi-permanently alter ridge channels between these six modules. It is worth noting that not characteristics. These factors also affect acquisition of all 11 vulnerability points are unique to the biometric system. fingerprints by the fingerprint sensor. The importance of Many of the same points (e.g., storage and communication quality is widely acknowledged, but there is no standard channels) are vulnerable in other authentication systems and means of assessing quality. The current standardization effort similar methods can be used to limit those particular for assessing quality for biometric samples refers to three vulnerabilities. different connotations of quality: The most publicized vulnerability in biometric systems * Character resides at the data collection module in the form of spoofmg or * Fidelity presenting artificial representations of biometric samples (see * Utility module #1, Fig. 1). If an artificial or fake biometric sample is These three connotations of biometric sample quality can be accepted by the biometric system at this initial stage, the entire directly applied to fingerprint sample quality. Character is a biometric system is corrupted and the system is compromised. description of quality based on inherent features from the Attacks on biometric systems are not new, popular culture source of the fingerprint. Individuals who have scarred seeks to circumvent security systems and biometric systems fingerprints or dry or cracked skin on the fingertips will are not immune. Several online resources are available that provide samples with poor character. Fidelity is a description describe such attacks on the data collection module, and many of quality based on degree of similarity between the actual movies and television shows have highlighted attacks on such fingerprint and the fingerprint image acquired by the sensor. systems. One such attack at this data collection module was Inconsistent contact with the fmgerprint sensor can lead to outlined in the work of Matsumoto, Yamada, and Hoshino fingerprint samples with poor fidelity. Utility is a description (2002) using "Gummy Fingers" [1]. of quality based on observed or predicted contribution of the The biometric research community, as well as industry, has fingerprint sample to the overall performance of the focused its research on preventing such attacks by using the fingerprint recognition system. Utility of a fmgerprint sample concept of "liveness" detection techniques. Today, the newer is directly affected by the character and fidelity of the sensors are improving their resilience against a spoofing attack fingerprint sample, and should be the closely related to at this module. In the past, acetate spoofing attacks - where performance of the recognition system. an image of a fingerprint placed on acetate was accepted as a A substantial amount ofresearch has been conducted in area genuine live finger - was easy to reproduce. Now, such of quality assessment, all of which give varying levels of attacks are proving increasingly difficult to succeed, hence the importance to character, fidelity, and utility. Previous research more complicated approaches to attacks being waged on the in the field of fingerprint image quality assessment can be vulnerable sensor. Techniques for liveness detection within generalized into three categories: local features analysis, the fingerprint modality focus on moisture content, global features analysis, and quality analysis as a classification temperature, electrical conductivity, and challenge response. problem [10]. Features of the fingerprint image such as minutiae count, fidelity of minutiae, contrast ratio between III. FINGERPRnT IMAGE QUALITY ANALYSIS ridges and valleys, capture area of the figerprint, and The purpose of this research paper was not to prove the detennination of dominant direction are used by quality vulnerability of the biometric system, but to examine the algorithms in varying capacities to make quality repeatability of the features of the gelatin finger print as determinations. For the purpose of this research, fingerprints compared to the live genuine sample once the image has been from live fngers and gelatin fingers were examined by two acquired. The research question is whether an artificial print different image quality algorithms, one provided by Aware, captured on an optical sensor exhibits any of the same Inc. and the other provided as a part of NIST Fingerprint characteristics as a genuine fingerprint from the same Imaging Software (NFIS). individual captured on the same sensor, and whether any distinguishers might enable the artificial print to be excluded IV. METHODOLOGY later on in the process, if the initial data collection module This research involved two separate experiments. The first accepts it. The research also investigates whether, over time, 31 Authorized licensed use limited to: Purdue University. Downloaded on February 18,2010 at 15:00:20 EST from IEEE Xplore. Restrictions apply.
    • experiment was conducted in two stages. Stage 1 of the first verified by the software, then the mold would have been experiment involved creation of a set of images from an destroyed and the process started again. artificial gelatin fimger that was crafted from the same subject The artificial finger was returned to the refrigerator in a who provided the genuine fingerprint. The procedures to simple (airtight, but not vacuum-sealed) plastic storage accomplish this feat required adaptation of several different container for 48 hours at a temperature of 2TC. This procedure methodologies outlined in the literature for creating an allowed the gelatin finger to completely solidify to its artificial fingerprints, including the work done by Matsumoto, permanent state. Yamada, and Hoshino [1]. Prior to creating the mold, the After removing the gelatin finger from the refrigerator, tests following necessary ingredients and utensils were gathered: were conducted on it to estimate the optimal load required for plastic clay, hot water, and a pair of plastic tongs. To create acquiring images. In general terms, it is best to use the least the mold, a quantity of plastic clay sufficient to cover the weight possible to produce a scan in order to minimize the genuine finger was required. In order to make the plastic clay spreading and dissolution of the gelatin finger's valleys and malleable, it was placed briefly in boiling water. In order to be ridges. Testing of loads ranging from 20Og to 1,000g, as utilized, the plastic clay had to have a consistency such that it measured by a Tanita digital scale, was performed. enabled a mold to be created by placing the finger with only Approximately 200g was determined to be the lower limit to light pressure. When the plastic clay has attained a sufficient produce an image, with 550g being the upper limit before level of pliability, the clay was cooled. Once the clay had distortion and inability to match occurred. The next stage of cooled enough to be touched, the finger was placed into the the experiment involved acquisition of a series of images from the gelatin finger. All of the images were acquired from the clay to a depth sufficient to create the mold to be used to craft optical sensor using the gelatin fimger over a 15-minute time the gelatin finger. The genuine finger was kept in the plastic period. After 15 minutes of acquiring images, the gelatin clay until the clay had cooled enough to retain its shape and finger had degraded to the point at which it was no longer able the details of the genuine finger. After the finger was to be accepted by the optical sensor. In all, 163 images were removed, the mold was allowed to cure for an additional 10 produced over the 15-minute time period. A detailed minutes. The resulting mold for this study is shown in Fig. 2. description of the 160 images utilized in the study is provided in the results section. Stage 2 of the first experiment called for the collection of a series of live samples. One hundred sixty live samples were acquired from the same finger that was used to create the artificial gelatin finger. The live samples were collected over an 8-minute time period on a commercially available optical sensor. The authors chose to collect 160 live samples, as this was the same number of fingerprints collected from the gelatin finger. These 160 live images were all stored according to the time collected; they are used to provide a baseline quality assessment that will be compared against the samples Fig. 2 Plastic mold formed to create gelatin finger generated by the gelatin finger. After data collection, both sets of images (from the live The next step was to create a gelatin mixture capable of finger and the gelatin fimger) were processed through the NIST producing artificial fingerprints from the mold that would be Fingerprint Image Software (NFIS) package. The MINDTCT recognizable to the sensor. Two sheets of gelatin weighing function was used to count the number of minutiae present in 3.5g were soaked in cold water for five minutes. In order to each individual image. The NFIQ function was used to remove the excess water, the gelatin sheets were dried until evaluate image quality, which is determined on a rating scale the gelatin weighed 14-16g. Next, a bowl was immersed into of 1 to 5, with 1 being the best and 5 being the worst. The extremely hot water, and the gelatin was placed into the bowl results of M1NDTCT and NFIQ from both groups (the live to soften and melt the gelatin. Once the gelatin had melted, it fingerprints and the gelatin fingerprints) were then compared was poured into the clay mold. Immediately after placing the by the means of statistical t-tests (using an a level of 0.05) to gelatin in the mold, the mold was placed in a refrigerator to determine if any statistical difference existed across the cool for 10 minutes at a temperature of 1°C. Cooling groups. Aware, Inc. offers a commercially available image transforms the gelatin to a state that is resistant to changes in quality and minutiae count software; this software was used to shape when touched. Ambient room temperate, when this extract image quality scores and minutiae counts for experiment was conducted, was 220C. fingerprints from the live finger and gelatin finger groups. By After the gelatin finger had cured in the refrigerator for a utilizing two different software packages to analyze the period of approximately one hour, it was removed from the fingerprints, we sought to eliminate bias that might have been clay mold and placed on the sensor to determine whether it generated by utilizing only a single application. was actually able to produce images. Ten attempts to acquire The second experiment involved collecting fingerprint an image were made; in all 10 instances, an image was images from left index finger and the right thumb from 30 produced. The software verified the mold. If the mold was not different subjects. Each subject was asked to provide 20 32 Authorized licensed use limited to: Purdue University. Downloaded on February 18,2010 at 15:00:20 EST from IEEE Xplore. Restrictions apply.
    • images of each fimger on three different optical sensors. A of live and gelatin fingerprint minutiae count for last 16 prints. silicon mold was created the left index and right thumb of An interesting observation is that the minutiae count increases each subject, and the molds were used to provide 20 for the gelatin fingerprint group, but stabilizes for live fingerprint images on each of the three optical sensors. The fingerprints. procedures used in [1] were also employed in creating the silicon molds in the second part of the experiment. At the end Boxplot of AWAREJLive-comt, AWARE_Gelatincount of the data collection there were 3600 fingerprint images from 70. live fingers, and the 3600 fingerprint images from silicon fingers. 60* A. Experiment I V. RESULTS a 50I Creation and acquisition of images from gelatin fingers can be problematic, as previous research has shown that gelatin 40 _ fingers do not afford consistent repeatability. However, this study provides anecdotal evidence suggesting that better 3J preparation and storage of the artificial finger can aid in the repeatability of the images produced. The first 39 samples AWARE Live ca,t AWARELGelItkSem_t provided consistently successful spoofing results; on the 40th Fig. 4 Box plot, live and gelatin finger minutiae count using Aware presentation of the gelatin finger, a failure-to-acquire (FTA) QualityCheck resulted. Overall, 163 images were acquired, but only 160 images were used for the final study. Degradation on the final 3 images rendered the images unusable. The acquisition rate Boxplot of t.S_Live_Coumt, NFI&Gelatin cout for this particular gelatin fingerprint was 90.7%, producing a 110 FTA rate of 9.3%. Fig. 3 shows the gelatin print (left) a live print (right). The FTA rate for the live finger was 0.0%. 90- 900 a 0- 60- o 60 50 40 NRSLve_Cort NFS.Geeatincount Fig. 5 Box plot, live and gelatin finger minutiae count using NFIS M1NDTCT Fig. 3 Gelatin finger (left) and live fmger (right) The stabilization of minutiae count for the live fingerprints can probably be attributed to habituation or acclimation to the The minutiae count analysis on both the fingerprint groups device. The subject has been acclimated to placing the sample was performed. Fig. 4 and Fig. 6 show box plots of the finger on the optical sensor, which reduces the inconsistent minutiae count from the live finger and gelatin finger, contact of finger surface with the platen of the sensor. Another respectively, generated from Aware, Inc.'s image quality tool. interesting observation is the increase in minutiae count Fig. 5 and Fig. 7 show box plots of the minutiae count from between the gelatin fingerprints over time. This suggests the live finger and gelatin fmger, respectively, generated from degradation of the gelatin finger and mold because of NFIS's MINDTCT. repetitive use and introduction of cracks in the mold used to The results from the box plot graphs generated by both the create the gelatin finger. Evidence suggests that, over time, the Aware and NFIS software programs show that the live number of minutiae for the gelatin fingerprint increases, while fingerprints have a lower minutiae count than the gelatin the number of minutiae for the live fmgerprint stabilizes. fingerprints, which is most likely a result of indirect and inconsistent contact with the optical sensor. In order to study the deterioration of the gelatin fingerprints, the first 16 and last 16 samples from the live and gelatin fmgerprint groups were used. Fig 6 shows a box plot of the live and gelatin fingerprint minutiae count for first 16 prints, and Fig. 7 shows a box plot 33 Authorized licensed use limited to: Purdue University. Downloaded on February 18,2010 at 15:00:20 EST from IEEE Xplore. Restrictions apply.
    • 9 55 501 45 40 35 303 65- So 60- 55- 45- 40- 35- 30 Boxplot of Lisfe_First-16;, Gelatin-First-16 I ve-FirsL6 Boxplot of Live_Last_6, GelatinjLastL16 Live_Last_16 'I'-- Fig. 8 and Fig. 9 shows the scatter plots for minutiae count versus sample numbers of live and gelatin fingerprint groups. Both of these graphs give credence to the observations made from the box plots. Scatterplot of NFISJJve-Count, WISjGelatin_count vs rnageCount > 110 100 80 70 6.0 40 0 2 0 I 1 S%mN& 40 me_pmft U . Jj,#MO 6 U *- 0 " 100 12 1 14 U 1 Fig. 8 Scatter plot, minutiae count vs. sample number using NFIS MIINDTCT Gebtin_Fst16 Fig. 6 Box plot, live and gelatin finger minutiae count using Aware, first 16 prints Gelati_Last_16 Fig. 7 Box plot, live and gelatin finger minutiae count using Aware QualityCheck, last 16 prints - VariabLa -+- EIS Lwe.Co.wA -'- W- NFIS_GeaUn_cwnt (U 'U 9 Groups 70 60- 50- 40- 30- 70 so 80 40 Aware_LiveLIQ 0 Aware_Gelatin_IQ 160 NFIS_Live-IQ 160 NFIS Gelatin IQ 160 * 20 tE a ... Scatterplot of AWAREJive_ount, AWARE_Gelatincmnt vs bnaJeCoLnt ,V,able , 40 5 U0 0406 a 60 * ILI *rn * N i 60 ' ImageSotunt 160 w *u mA .* 80 100 320 140 160 180 Image.Eoumt Fig. 9 Scatter plot, minutiae count vs. sample number using Aware Image quality is another metric that was considered important for this research. Fig. 10 shows the scatter plot of image quality scores obtained using Aware, Inc.'s quality algorithm on the live and gelatin fingerprints. The graph of image quality scores clearly indicates a degradation of the gelatin fingerprint. T-tests of image quality scores between the live and gelatin fingerprints showed a statistically significant difference. The severe decrease in image quality noticed in the repeated use of the gelatin finger indicates that it would be of practical use only for the first 10 or so attempts. Table 1 shows the results from the t-tests. Scatterplot of AWAREjLiveJIQ, AWARE-Gelatn IQ vshnage-Count U. * | * * ** * U T-TEST RESULTS Interestingly, image quality scores might not provide a clear indication of a spoofing attempt, because in the initial nine samples, there was no statistically significant difference between the image quality score means ofthe two groups. _ a X- a 100 120 140 160 180 Fig. 10 Scatter plot, image quality scores using Aware QualityCheck TABLE I a 5 s UI *i t-&- Mean 79.22 61.0' 1.88 2.21 U l AWARE Live count -_U- AWAREfidati count -4- -U AWARE_i.veJq AWAREJ.I*anJ p-value <.05 <.05 34 Authorized licensed use limited to: Purdue University. Downloaded on February 18,2010 at 15:00:20 EST from IEEE Xplore. Restrictions apply.
    • B. Experiment 2 A paired t-test test for statistically significant difference was Using the Aware software, the minutiae count and quality conducted on image quality scores. The test showed a p-value scores were computed for all the live, and silicon fingerprints of < .05, which indicated that the difference in iimage quality collected in the second experiment. Table II shows the scores was statistically significant. The minutiae count for the summary statistics for image quality and minutiae count for dataset of silicon fingerprints and dataset of live fingerprints is the dataset of live and silicon fingerprints. very similar, but the quality scores for the two groups were significantly different. The results from this experiment TABLE II indicate that image quality scores from the silicon fingerprints SUMMARY STATISTICS are of medium quality, but they still are significantly different Groups N Median Median Minutiae from image quality of live fingers. This provides an Image Count interesting observation - the extraction of minutiae points is Quality Score very similar between silicon mold fngerprints and live Live Fingers 3600 80.0 39.0 fingerprint images, but the difference in quality scores points Silicon Fingers 3600 69.0 39.0 to noise in the ridge flow, and contrast levels between ridges Groups N Mean Image Mean Minutiae and valleys and other factors. This could also be possible due Quality Score Count to distortion and elastic deformation of the silicon fingerprint Live Fingers 3600 76.46 39.44 being different than the corresponding live fingerprint. This Silicon Fingers 3600 61.35 38.98 observation merits ftuther research into this phenomenon since the ridge flow and contrast analysis could be used as a Fig. 11 and 12 show the box plot of quality scores and differentiating factor. The spread of the image quality scores minutiae count respectively. The spread of the image quality of the silicon mold is also more variable than the images scores is a lot larger for silicone finger compared to live quality scores of the live fmgerprint images, which indicates fingers. degradation of the silicon fingerprints between successive attempts. Boxplot of Live Finger & Silicone Finger Image Quality Scores VI. CONCLUSION inn-I iw The danger with providing a recipe for spoofing is that an 80 attack methodology to a biometric sensor is revealed. However, in this case, the attack is analogous to an individual revealing a PIN number to a fraudster and accompanying the fraudster to the ATM to watch the fraudster withdraw the ii 240 individual's money. The test was not designed as a spoofing enquiry to evaluate the security of the system, but rather to understand the characteristics of the gelatin finger and silicon 20Q fmger compared to its live counterpart. Some interesting results were obtained as result of the 0 analysis in both the experiments. In the first experiment, the Live Finger Silio Finger gelatin finger was able to provide 163 samples with an Fig. 11 Box plot, image quality scores using Aware image quality acquisition rate of 90.7%. Further analysis of fingerprints from software the live and gelatin fingers showed a considerable difference in the basic characteristics between the two groups. Repeated use of the gelatin finger resulted in a rapid degradation of the Boxplot of Live Finger, Silicone Finger quality of prints provided, which was reinforced by an 100- * increase in minutiae count with repeated use. Expecting a gelatin finger to survive over a 100 attempts would be 80S unreasonable, but our analysis showed that even after 10 uses, the gelatin finger showed a severe degradation in quality, even 60- though the system matched the spoof. Stabilization of the 0 minutiae count for the live fingerprint was an unexpected A 40- result of the experiment, but it reaffins the notion of habituation and how it can be used to acquire fingerprint 20- samples representative of its owner. Comparing the minutiae count results of the first and second experiment, the minutiae O- count of silicon mold fingerprints was more similar to the live Live Mintae Count Silicone Minutae Count fingerprints compared to the gelatin mold fingerprints. A future work of this study is to perform matching operations Fig. 12 Box plot, Minutiae count between the live fingerprints and silicon fingerprints to examine an effect of quality and minutiae count on the 35 Authorized licensed use limited to: Purdue University. Downloaded on February 18,2010 at 15:00:20 EST from IEEE Xplore. Restrictions apply.
    • matching error rates. Results from both the experiments showed a statistically significant difference in image quality scores between the fake fingerprints and live fingerprints which could be used as an anti-spoofing mechanism. Understanding the characteristics of fake fingerprints is important in devising countermeasures, and extremely important in increasing security of fingerprint biometric systems. REFERENCES [11 T. Matsumoto, H. Matsumoto, K. Yamada, and S. Hoshino, "Impact of artificial 'gummy' fingers on fingerprint systems, i Proc. SPIE, vol. 4677, Optical Security and Counterfeit Deterrence Techniques IV, San Jose, 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 and privacy 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 reporting perfonnances of biometric devices," UK Biometric Working Group, City, ST, 2002, pp. 1-32. [5] U.S. Department of Homeland Security, Transportation worker identification credential (TWIC) program, Available online: https://www.twicprogram.com. [6] National Institute of Standards and Technology, Personal identity verification 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 data capture. 3rd ed., West Lafayette, IN: Copymat. 2005, p. 314. [8] A. Jam and N. Duta. "Deformable matching of hand shapes for user verification," 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 for fingerprint image quality computation," in 3rd COST 275 Workshop, Biometrics on the Internet, Hatfield, UK, 2005. 36 Authorized licensed use limited to: Purdue University. Downloaded on February 18,2010 at 15:00:20 EST from IEEE Xplore. Restrictions apply.