(2009) Statistical Analysis Of Fingerprint Sensor Interoperability
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(2009) Statistical Analysis Of Fingerprint Sensor Interoperability



The proliferation of networked authentication ...

The proliferation of networked authentication
systems has put focus on the issue of interoperability.
Fingerprint sensors are based on a variety of different technologies that introduce inconsistent distortions and variations in the feature set of the captured image, which makes the goal of interoperability challenging. The motivation of this
research was to examine the effect of fingerprint sensor interoperability on the performance of a minutiae based matcher. A statistical analysis framework for testing
interoperability was formulated to test similarity of minutiae count, image quality and similarity of performance between
native and interoperable datasets. False non-match rate (FNMR) was used as the performance metric in this research.
Interoperability performance analysis was conducted on each sensor dataset and also by grouping datasets based on the
acquisition technology and interaction type of the acquisition sensor. The lowest interoperable FNMR observed was 0.12%.



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(2009) Statistical Analysis Of Fingerprint Sensor Interoperability Document Transcript

  • 1. Statistical Analysis of Fingerprint Sensor Interoperability Performance Shimon K. Modi, Stephen J. Elliott, and Hale Kim Abstract—The proliferation of networked authentication distributed & multi-vendor architectures become more systems has put focus on the issue of interoperability. pervasive. Fingerprint sensors are based on a variety of different This paper examines interoperability from the perspective technologies that introduce inconsistent distortions and of fingerprint sensors. This research defined fingerprint sensor variations in the feature set of the captured image, which makes interoperability as the ability to match fingerprints of the same the goal of interoperability challenging. The motivation of this individual collected from different sensors. Fingerprint research was to examine the effect of fingerprint sensor interoperability on the performance of a minutiae based sensors are based on a variety of different technologies like matcher. A statistical analysis framework for testing electrical, optical, thermal etc. The physics behind these interoperability was formulated to test similarity of minutiae technologies introduces inconsistent distortions and variations count, image quality and similarity of performance between in the feature set of the captured image, which makes the goal native and interoperable datasets. False non-match rate (FNMR) of interoperability even more challenging. A fingerprint was used as the performance metric in this research. recognition system deployed in a distributed architecture can Interoperability performance analysis was conducted on each benefit from gaining a deeper insight into interoperability of sensor dataset and also by grouping datasets based on the sensors and its effect on error rates. The purpose of this acquisition technology and interaction type of the acquisition research was to examine the effect of sensor dependent sensor. The lowest interoperable FNMR observed was 0.12%. variations and distortions, and characteristics of the sensor on the interoperability matching error rates of minutiae based I. INTRODUCTION fingerprint recognition systems. This study focused on an Authentication of individuals is a process that has been exclusive aspect of the problem space - the acquisition performed in one form or another since the beginning of subsystem. The end objective of this study was to provide recorded history. Whilst establishing and maintaining the greater insight into the effect of a fingerprint dataset acquired identity of individuals has been ongoing since this time from various sensors on performance measured in terms of accurate automated recognition is becoming increasingly false non match rates (FNMR). important in today’s networked world. As technology advances, the complexity of these tasks has also increased. II. LITERATURE REVIEW Digital identities and electronic credentialing have changed Performance of a biometric system can be affected by the the way authentication architectures are designed. Instead of errors introduced in the data capture subsystem of the general stand-alone and monolithic authentication architectures of the biometric model [1]. These factors can be attributed to either past, today’s networked world offers the advantage of the user or sensor. The human interaction error – that is the distributed and federated authentication architectures. The interaction between the individual and the sensor is described development of distributed authentication architectures can be in depth in [2]. Whilst [2] discusses the human biometric seen as an evolutionary step, but also raises the issue always sensor interaction, this paper examines the variability accompanied by an attempt to mix disparate systems: introduced by the sensor. Sensor variability results in moving Interoperability. This issue is of relevance to all kinds of the distribution of genuine scores away from the origin, authentication mechanisms, and biometric recognition thereby increasing error rates and negatively impacting the systems in particular. The last decade has witnessed a huge performance of the system [3]. There are many challenges that increase in deployment of biometric systems, and while most occur when matching two fingerprints, which are outlined in of these systems have been single vendor, monolithic [4]. These include existence of spurious features and missing architectures the issue of interoperability is bound to arise as features compared to the database template, transformation or rotation of features, and elastic deformation of features. Manuscript received June 07, 2009. Ko and Krishana presented a methodology for measuring S.K. Modi,Ph.D, is the Director of Research of the Biometric Standards Performance and Assurance Laboratory, Purdue University, West Lafayette, and monitoring quality of fingerprint database and fingerprint IN 47907 USA (phone: 765-494-0298; e-mail: modis@purdue.edu). match performance of the Department of Homeland H. Kim, Ph.D., is a professor with School of Information and Security’s Biometric Identification System [5]. They Communication Engineering, INHA, Incheon, Korea. (e-mail: proposed examining fingerprint image quality not only as a hikim@inha.ac.kr). S.J. Elliott, Ph.D., is an associate professor with the Industrial Technology predictor of matcher performance but also at different stages Department, and Director of the Biometric Standards Performance and in the fingerprint recognition system. They pointed out the Assurance Laboratory, Purdue University, West Lafayette, IN 47907 USA importance of understanding the impact on performance if (e-mail: Elliott@purdue.edu). Authorized licensed use limited to: Purdue University. Downloaded on December 14, 2009 at 17:29 from IEEE Xplore. Restrictions apply.
  • 2. fingerprints captured by a new fingerprint sensor were III. METHODOLOGY integrated into an existing identification application. Their observations and recommendations were primarily aimed at A. Experiment Setup facilitating maintenance and matcher accuracy of large scale A wide cross-section of commercially available fingerprint applications. sensors was chosen for this study. Three different Jain and Ross investigated the problem related to technologies and two different interaction types were selected fingerprint sensor interoperability, and defined sensor – Table I outlines the technical details. 190 subjects were interoperability as the ability of a biometric system to adapt recruited, consisting of 131 males and 59 females – other raw data obtained from different sensors [6]. They defined the demographic characteristics are shown in Table II. Each problem of sensor interoperability as variability introduced in subject provided six images of their index finger on their the feature set by different sensors. They collected fingerprint dominant hand. The order of the sensors was randomized for images on an optical sensor manufactured by Digital each subject to minimize the learning effect for interaction Biometrics and a solid state capacitive sensor manufactured with the sensors. by Veridicom. The fingerprint images were collected on both TABLE I sensors from 160 different individuals who provided four SENSOR CHARACTERISTICS impressions each for right index, right middle, left index, and Sensor Technology Type Interaction Capture Type Area (mm) left middle finger. Their results showed that EER of 6.14%for D1 Thermal Swipe 14 X .4 matching images collected from Digital Biometrics sensor and D2 Capacitive Swipe 13.8 X 5 EER of 10.39% for matching images collected from D3 Optical Touch 30.5 X 30.5 Veridicom sensor. The EER for the matching images D4 Optical Touch 14.6 X 18.1 collected from Digital Biometrics sensor to Veridicom sensor D5 Capacitive Touch 12.8X15 was 23.13%. Their results demonstrated the impact of sensor D6 Optical Touch 16 X 24 interoperability on matching performance of a fingerprint D7 Optical Touch 15 X 15 system. Nagdir and Ross proposed a non-linear calibration D8 Capacitive Touch 12.8 X 18 scheme based on thin plate splines to facilitate sensor interoperability for fingerprints [7]. Their calibration model TABLE II was designed to be applied to the minutiae dataset and to the DATASET DESCRIPTION fingerprint image itself. They used the same fingerprint Total 190 Subjects dataset used in the study conducted by [6] but used the Gender Male Female VeriFinger minutiae based matcher and BOZORTH3 minutiae based matcher for the matching fingerprints. They 131 59 applied the minutiae and image calibration schemes to Occupation Manual Office Worker fingerprints collected from Digital Biometrics sensor and Laborer Veridicom sensor and matched the calibrated images from the 17 173 two sensors against each other. Their results showed an Number of 190*6 = 1140 increase in Genuine Accept Rate (GAR) from approximately samples 30% to 70% for the VeriFinger matcher after applying the minutiae calibration model. For the BOZORTH3 matcher an increase in GAR from approximately 35% to 65% was B. Data Processing Methodology observed. The variables analyzed for this study were minutiae count The National Institute of Standards and Technology (NIST) and image quality score of the fingerprint image. Minutiae performed an evaluation test to assess the feasibility of count was generated for each dataset using the VeriFinger 5.0 interoperability of INCITS 378 templates [8]. The minutiae extractor. Image quality scores were generated using NFIQ. template interoperability test was called the MINEX 2004 Minutiae count scores were always greater than 0, and NFIQ test. The MINEX report identified quality of the datasets as a scores ranged from one to five where one indicated best factor which affected level of interoperability. The DOS and possible score and five indicated the worst possible score. DHS datasets were of lower quality and did not exhibit a level The VeriFinger 5.0 matcher was used to generate genuine of interoperability as that of POEBVA and POE databases. match and imposter non-match scores. Another paper proposed to solve the problem of acquisition mismatch using class conditional score distributions specific C. Data Analysis Methodology to biometric devices [9]. Their problem formulation depended Hybrid testing, which combined live acquisition and offline on class conditional score distributions, but these probabilities matching, was performed to analyze the data collected in this were not known a-priori. They devised an approach which experiment [10]. A hybrid testing scenario was necessary for used observed quality measures for estimating probabilities of this experiment because live subjects would not want to sit matching using specific devices. Their device specific score through combinatorial use of multiple sensors. Genuine and normalization procedure reduced the probability of mismatch imposter match scores were generated offline after all 190 for samples collected from different devices. subjects had completed their data collection sessions. The six fingerprint images provided by the subjects were split into two Authorized licensed use limited to: Purdue University. Downloaded on December 14, 2009 at 17:29 from IEEE Xplore. Restrictions apply.
  • 3. groups: the first three images were placed in an enrollment H20: μs score = μr score for all s (2) dataset and the last three images were placed in the test H2A: μs score r score for all s dataset. Enrollment and test template datasets were created for where s = 1..8, r = 1..8 datasets all eight sensors using VeriFinger 5.0. The resulting enrollment templates from each dataset were compared Previous sensor interoperability studies have compared the against templates from each test dataset resulting in a set of numerical FNMR of native and interoperable datasets. The scores S, where test of homogeneity of proportions was used to test similarity of FNMR. The main objective of this test was to examine if S = {(Ei,Vj,scoreij)} the difference in FNMR among the datasets was statistically i= 1,..,number of enrolled templates significant. This test aided in statistically testing equality of j = 1,.., number of test templates FNMR for native datasets and interoperable datasets. The scoreij = match score between enrollment template and test template critical value of 2 was computed at a significance level of 0.05 and degrees of freedom (n-1), and then compared to the Match score analysis was conducted by computing test statistic 2. If the test statistic exceeded the critical value, performance matrices, consisting of FNMR for all datasets the null hypothesis was rejected. If the null hypothesis was collected in the experiment at the fixed FMR of 0.1% [11] as rejected, further analysis was performed to examine which shown in Fig. 1. interoperable datasets caused the rejection. This was done using the Marascuillo procedure (for a significance level of Sensor 1 Sensor n 0.05) simultaneously tests the differences of all pairs of E proportions for all groups under investigation [13]. N R Template IV. ANALYSIS OF RESULTS O Generator L L A. Minutiae Count Template M Table III shows the descriptive statistics for minutiae count. E N The omnibus test for main effect of sensor dataset was T DB DB statistically significant p < 0.001 at = 0.05. Tukey’s HSD Match test was performed to determine the statistical significance of Matcher Score difference between each possible pair in the group of datasets. All pairwise comparisons were found to be statistically significant in their differences. DB DB T TABLE III E MINUTIAE COUNT S Template Sensor Mean Standard T Deviation I Template D1 41.72 10.57 N Generator G D2 28.32 10.73 D3 40.25 10.12 D4 30.74 8.06 Sensor 1 Sensor n D5 24.38 6.87 D6 38.62 9.18 Fig. 1. Score Generation Methodology D7 27.53 7.69 D8 26.15 6.73 The fingerprint feature analysis involved examination of B. Image Quality minutiae count and image quality scores. Statistical tests were performed to test similarities in minutiae counts and image Table IV shows the descriptive statistics for image quality quality between all of the fingerprint datasets. If a statistically scores generated by NFIQ. significant difference was observed for the test, all possible TABLE IV pairs of means were compared. Tukey’s Honestly Significant NFIQ SCORES Difference (HSD) was used to test all pairwise mean Sensor Mean Median comparisons. Tukey’s HSD is effective at controlling the D1 1.29 1 overall error rate at significance level , and thus preferred D2 1.91 2 over other pairwise comparison methods [12]. D3 1.75 1 D4 2.00 2 H10: μs minutiae_count = μr minutiae_count for all s r (1) D5 2.18 Eq. 3.1 2 H1 A: μs minutiae_count μr minutiae_count for all s r D6 1.77 2 D7 2.03 2 D8 1.58 2 Authorized licensed use limited to: Purdue University. Downloaded on December 14, 2009 at 17:29 from IEEE Xplore. Restrictions apply.
  • 4. NFIQ uses a 3-layer feed forward nonlinear perceptron The first cell in the rows in Table VI indicated the native model to predict the image quality values based on the input dataset and the remaining cells in that row indicated its feature vector of the fingerprint image [14]. Neural networks corresponding interoperable datasets. A p value of less than are non-parametric processors, which implies that the results 0.05 indicated a statistically significant difference, indicated produced would have non-parametric characteristics [15]. by S in the table. NS indicates a non significant difference. This property of NFIQ values precluded the use of parametric The pairwise comparisons test showed an even distribution of based approach for detecting differences in quality scores similarity of FNMR with interoperable datasets without any between all fingerprint datasets. Instead an analysis of apparent trend among acquisition technologies and interaction variance on rank of the response variable was performed using types of the sensors that the datasets were collected from. The the Kruskal Wallis test. The test for main effect of the sensor test of {D2, D1} was interesting since it did not show a was statistically significant at = 0.05. Follow up tests were difference in the pairwise test of proportions but showed a performed on pairwise comparisons of sensor datasets to difference in minutiae count and image quality scores for all determine which pairs of sensors were statistically significant software used. D2 was collected a capacitive swipe sensor and in the differences of NFIQ scores. Tukey’s HSD pairwise D7 was collected using an optical touch sensor. The test of comparisons were performed on the ranks of the observations {D3,D4} was interesting since it did not show a difference in for each dataset. The test of pairwise comparison for pairwise test of proportions, and also did not show a {D4,D7}; {D5,D8}; and {D7,D8} was found to be not difference in minutiae count and image quality scores. D3 and statistically significant at = 0.05. As a group, neither optical D4 were both collected using optical touch sensors. These two touch sensors nor capacitive touch sensors showed a high tests indicated that impact of minutiae count similarity and level of similarity of quality scores. image quality score similarity was not consistent on the pairwise test of proportions. Interoperable datasets which C. FNMR Analysis contained D8 as its second dataset showed a high level of The interoperability FNMR matrices are shown in Table V. similarity to the native datasets which were used to create the The cells along the diagonal indicate enrollment and test interoperable dataset. This indicated that D8 did not degrade fingerprint images from the same fingerprint sensor. The cells the performance of an interoperable dataset compared to the off the diagonal indicate fingerprint images from different performance of native datasets. sensors. The sensor dataset in the rows indicate the source of the enroll sensor and the sensor dataset in the columns indicate TABLE VI the source of the test sensor. All the native FNMR were found PAIRWISE COMPARISON OF PROPORTIONS to be lower than interoperable datasets except for the D1 D2 D3 D4 D5 D6 D7 D8 interoperable dataset {D5,D8} where D5 and D8 were both D1 S S S S S S S collected from a capacitive touch type sensor. The D2 NS NS NS S S S NS interoperable FNMR for {D5, D8} = 0.49% and native D3 S S NS NS S S NS D4 S S NS NS S S NS FNMR for D5 = 0.78% as shown in Table V. The dataset for D5 S S NS NS S NS NS D8 had a mean minutiae count of 26.15 while dataset for D5 D6 S S S NS S NS NS had a mean minutiae count of 24.38. This result indicated the D7 S S NS NS NS NS NS interoperable dataset {D5, D8} had a larger number of D8 S S NS NS NS S NS minutiae points to match compared to native dataset of D5. The capture area of the sensor used to for D8 dataset was D. Impact of Image Quality larger compared to capture area used for D5 dataset. A cross This section describes the impact of removing low quality reference analysis of the FNMR matrix with similarity of fingerprint images and recalculating the interoperable FNMR. minutiae count and quality scores from the previous section The fingerprint images which had NFIQ quality scores of four did not show any specific relations. and five were not used for the matching operation and the The Marascuillo procedure was used to simultaneously test results are shown in Table VII. NFIQ provides five categories the differences of all pairs. The results for pairwise of quality scores and experiments have been performed by comparison for each native dataset are shown in Table VI. NIST to predict the impact of NFIQ quality scores on matching operations. The NIST MINEX test report indicated TABLE V that a reduction in interoperability FNMR would be observed FNMR AT FIXED FMR 0.1% since poorly performing images were not used [8]. The D1 D2 D3 D4 D5 D6 D7 D8 analysis performed in this section, examined the impact of D1 0.47 6.79 5.60 4.44 5.61 31.03 11.30 3.76 removing low quality images on variance of interoperability D2 7.33 5.05 6.49 7.44 10.92 16.18 10.83 6.11 FNMR. D3 8.57 4.78 0.24 1.07 1.28 2.73 1.79 0.54 D4 5.10 4.44 0.95 0 1.39 1.60 1.83 1.08 Note that the interoperable FNMR for {D1,D6} increased D5 5.56 7.64 0.85 0.85 0.78 4.42 2.23 0.42 to 0.267% compared to the full dataset interoperability FNMR D6 33.86 13.42 2.99 1.30 4.47 0.17 1.41 2.15 of 0.25%. D1 dataset was collected using a thermal swipe D7 14.92 9.89 1.73 2.12 2.35 2.71 0.94 1.85 sensor and D6 dataset was collected using an optical touch D8 2.87 2.96 0.12 0.90 0.49 1.73 0.77 0.11 sensor. This was an interesting result since all the other FNMR Authorized licensed use limited to: Purdue University. Downloaded on December 14, 2009 at 17:29 from IEEE Xplore. Restrictions apply.
  • 5. reduced compared to FNMR calculated for full datasets. The FMR of 0.1%. The scatter plot only contained interoperable Marascuillo procedure was repeated on interoperable FNMR and interoperable dataset core overlap percentage fingerprint datasets which contained fingerprint images with since interoperable FNMR were the data points of interest. An NFIQ score of one, two or three. The overall test of inverse relation between FNMR and percentage of pairs with proportions was found to be significant in their difference at core overlap was observed. There were two data points of = 0.05. interest which represented interoperable datasets {D1,D6} TABLE VII and {D6,D1}. The relation between percentage of images with FNMR AT FIXED FMR 0.1% FOR IMAGE WITH NFIQ < 4 D1 D2 D3 D4 D5 D6 D7 D8 cores and FNMR of these two interoperable datasets did not follow the trend observed in the other interoperable datasets, D1 0.001 0.050 0.038 0.033 0.038 0.267 0.097 0.028 where a higher percentage of core overlap between images D2 0.055 0.046 0.057 0.060 0.098 0.150 0.098 0.051 was correlated to a lower FNMR. D1 was collected using D3 0.065 0.033 0.000 0.008 0.006 0.022 0.007 0.002 thermal swipe sensor and D6 was collected using optical touch D4 0.037 0.036 0.007 0.001 0.01 0.014 0.011 0.007 sensor. These points indicate existence of other underlying D5 0.046 0.066 0.005 0.006 0.004 0.040 0.015 0.003 factors which affected the interoperable FNMR. D6 0.306 0.116 0.019 0.011 0.032 0.000 0.012 0.012 D7 0.119 0.084 0.007 0.007 0.012 0.019 0.004 0.007 D8 0.025 0.026 0.000 0.007 0.004 0.013 0.007 0.001 The results from pairwise comparisons are shown in Table VIII. The recalculated pairwise comparisons showed that the test of proportions for {D3,D7} dataset did not show any statistically significant difference compared to results from Table VI. D3 and D7 both collected using optical touch sensors. This test indicated that removing the low quality Fig. 2. Core overlap scatterplot images reduced the difference of variance of FNMR between F. Interoperability at Interaction and Technology Level these datasets. A follow up evaluation of interoperability was performed The recalculated results for {D6,D4}, {D6,D7}, and by grouping the datasets into three categories: datasets {D6,D8} datasets showed a statistically significant difference. collected using swipe interaction type sensors, datasets The results from Table VI of the same test showed no collected using optical touch type sensors, and datasets statistically significant difference between the native and collected using capacitive touch type sensors. The evaluation interoperable datasets. This result was interesting as it of different groups allowed for examination of interoperability indicated that difference of variance of FNMR increased for at the acquisition and interaction level, not the sensor level. these interoperable datasets when the lowest NFIQ score D1 and D2 were placed in the Swipe group. D3, D4, D6 and images were removed. It should also be noted that D4, D6, and D7 were placed in the Optical Touch group. D5 and D8 were D7 were all optical touch sensors. Removal of low quality placed in the Capacitive Touch group. images did not have a consistent effect on FNMR of interoperable datasets which were all collected using optical TABLE IX touch sensors. ACQUISITION AND INTERACTION LEVEL INTEROPERABILITY AT FIXED FMR 0.1% TABLE VIII TEST PAIRWISE COMPARISON OF PROPORITIONS Swipe Optical Capacitive D1 D2 D3 D4 D5 D6 D7 D8 Touch Touch D1 S S S S S S S E Swipe 4.81 11.75 6.58 D2 NS NS NS S S S NS N R Optical 11.93 1.53 1.89 D3 S S NS NS S NS NS O Touch D4 S S NS NS S NS NS L Capacitive 4.76 1.47 0.45 D5 S S NS NS S NS NS L Touch D6 S S S S S S S D7 S S NS NS NS NS NS The {Capacitive Touch, Optical Touch} interoperable D8 S S NS NS NS S NS dataset showed the lowest interoperable FNMR indicating a high level of interoperability between optical touch and E. Core Overlap capacitive touch technologies. The {Capacitive Touch, A scatter plot (Fig. 2) was created where the x-axis Swipe} interoperable dataset had a lower FNMR than the represented the percentage of fingerprint image pairs with Swipe native dataset which indicated that the interoperable core overlap from each interoperable dataset and the y-axis dataset performed better than the native dataset. The {Swipe, represented its corresponding interoperable FNMR at fixed Optical Touch} dataset had the highest FNMR and indicated Authorized licensed use limited to: Purdue University. Downloaded on December 14, 2009 at 17:29 from IEEE Xplore. Restrictions apply.
  • 6. the lowest degree of interoperability. The results showed that interoperable datasets performance. The dataset collected for Capacitive Touch dataset had the lowest interoperable FNMR this research can be used for evaluating interoperability of compared to Optical Touch and Swipe interoperable datasets. sensors, feature extractors, and feature matchers as part of the The interoperable datasets generated with D8 dataset showed same experiment. A combined analysis of fingerprint sensors, a high level of similarity of FNMR with the other native feature extractors and feature matchers would further the datasets. Since D8 was part of the Capacitive Touch group it general understanding of this field. Further work into showed a better interoperable FNMR with other groups. In transforming the image so that the distortion of fingerprint combination with the sensor level interoperability analysis, images would be reduced without having any a-priori these results show the effect of interoperability at the knowledge about the fingerprint sensors would be of immense acquisition and interaction level. benefit to the field of fingerprint recognition. Sensor agnostic transformation methods, even if limited in its capabilities, V. CONCLUSIONS & FUTURE WORK would significantly augment the current methodologies being It was observed that similarity of minutiae count of the investigated. different sensor datasets did not show a relation to a specific acquisition technology or interaction type. Fingerprint images REFERENCES collected from optical touch sensors showed a higher level of [1] ISO, ISO/IEC 19795-1: Information technology - Biometric similarity in quality scores with fingerprints collected from performance testing and reporting - Part 1: Principles and framework, other optical touch sensors. The combination of similarity of ISO/IEC, Editor. 2006, ISO/IEC JTC 1/SC37: Geneva. minutiae count and image quality scores did not have an [2] Kukula, E.P., et al., Effect of Human Interaction on Fingerprint impact on similarity of FNMR for native and interoperable Matching Performance, Image Quality, and Minutiae Count. datasets. Higher minutiae count also did not have an impact on International Journal of Computer Applications in Technology, 2009. 34(4): p. 270-277. FNMR of its corresponding interoperable datasets. From the [3] Wayman, J. A Generalized Biometric Identification System Model. in observed results performance of interoperable datasets could 31st Conference on Signals, Systems and Computers. 1997. Pacific not be predicted by separately analyzing performance of Grove, California. native datasets. Interoperable datasets which had a higher [4] Bolle, R. and N.K. Ratha. Effect of Controlled Image Acquisition on Fingerprint Matching. in 14th International Conference on Pattern percentage of pairs of fingerprint images in which a core was Recognition. 1998. Brisbane, Australia. detected had a positive relation with lower FNMR, with the [5] Ko, T. and R. Krishnan. Monitoring and Reporting of Fingerprint only exception of {D1,D6} and {D6,D1} interoperable Image Quality and Match Accuracy for a Large User Application. in datasets. Consistent interaction of the finger with a sensor is Applied Imagery Pattern Recognition Workshop. 2004. Washington, D.C.: IEEE Computer Society. an important factor in improving performance, and it becomes [6] Jain, A. and A. Ross, eds. Biometric Sensor Interoperability. BioAW even more important when the fingerprints are captured from 2004, ed. A. Jain and D. Maltoni. Vol. 3067. 2004, Springer-Verlag: different sensors. A simple metric for consistent placement is Berlin. 134-145. required for collecting fingerprints from different sensors, and [7] Nagdir, R. and A. Ross. A Calibration Model for Fingerprint Sensor Interoperability. in SPIE Conference on Biometric Technology for this research showed that core overlap provides a metric Human Identificaiton III. 2006. Orlando, USA. which has an impact on FNMR of interoperable datasets. 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The statistical analysis framework can be modified to Gaithesburg, MD. p. 217. test for FMR of interoperable datasets, and it would be [15] Shu-Long, J., S. Zhong-Kang, and W. Yan-Yan. The Non-Parametric Detection with Neural Network. in International Conference on interesting to understand the impact of interoperability on Circuits and Systems. 1991. China: IEEE. FMR of fingerprint datasets collected from multiple sensors. The impact of removing low quality images from interoperable datasets did not lead to a higher level of similarity in FNMR between interoperable datasets and native datasets. The results indicated that further work is required to investigate the impact of quality on the variance of Authorized licensed use limited to: Purdue University. Downloaded on December 14, 2009 at 17:29 from IEEE Xplore. Restrictions apply.