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Abstract—There has been an increase in biometric application and advancement, and researchers continuously move to improve the technology. Fingerprint recognition is one of the biometric modalities that has experienced this growth, with its increasing presence in Homeland Security and law enforcement. This study investigated the subject performance movement within a fingerprint recognition system. The performance of a biometric system can be tied to the population using it. Analysis of the population brings context and granularity to performance results. This study analyzed fingerprint data collected by the International Center for Biometric Research (ICBR) back in 2010. DET curves and Zoo plots were gathered and segregated by finger and force. Performance data and error rates of different force level were compared to find the optimum and most meager conditions for each finger.

Index Terms—biometrics, fingerprint recognition, human-computer interaction, zoo menagerie, biometric performance.

I. INTRODUCTION

iometrics is the automatic identification of individuals using unique physiological and biological traits.Biometrics has traditionally been used by Law

Enforcement and Homeland Security but has found increasing traction in private industry [1]. This study aims to look at population factors for continuous improvement on the fingerprint recognition system utilized by these government agencies. Being biological in nature, population factors can contribute to performance variance. Analyzing the population performance under different force levels can add granularity to traditional performance analysis, such as detection-error tradeoffs (DET) and equal error rates (EER) [2].

II. LITERATURE REVIEW

A. BiometricsBiometrics refers to technologies that measure and analyze

human body characteristics for authentication purposes [3].Biometrics is a type of technology that is used for auto identification and capturing data from users for identification and verification processes relating to their identity and identity management [4]. When biometrics was first being created, the technology available was very limited, it was not until the 1960s when commercial biometric research began. The technology

was further advanced and refined in the 1970s and 1980s and then commercialized during the 1990s. Biometrics is now widely used to manage the risk of security breaches and facilitate transactions.

While there are many types of biometrics, each has their strengths and weaknesses. The “best” choice of biometrics is largely dependent on the requirements of the application. The various types of biometric systems can be contrasted based on factors encompassed by features like distinctiveness, stability, scalability, usability [4].

B. Fingerprint RecognitionA fingerprint recognition system uses an individual’s

fingerprint scan to identify the user. Originally called Galton points, minutiae are specific locations on a fingerprint that help uniquely identify a fingerprint image, and verify its associated user. These points are ridges, ridge endings, raised portions on the surfaces of the fingers, and bifurcations (a point at which two ridges meet) [4].

Fingerprints can be divided into three separate classifications based upon the ridge patterns that make up a person's fingerprint. These classifications are loops, whorls, and arches. The percentage of the population within each class is not equal, 60-65% of the population has loops, 30-35% has whorls, and the remaining 5-10% has arches. There are subclasses within each class, such as a plain arch versus a tented arch, and each subclass has unique properties that set it apart within the class [4].

In matching classifications, comparisons of the minutiae of a print are used to find a genuine match score and impostor match score. The genuine match is a statistic that measures how well an individual matches against their previously captured scans. An impostor match is a statistic that measures how well an individual can be distinguished from the captured scans of others [3].

C. Biometric PerformanceThe biometric performance of a population or system is

typically measured using various metrics, such as accuracy, efficiency, scalability, and template size [5]. Two

Subject Movement at Different Force Levels in a Fingerprint Recognition System

Kevin Chan, Andrew Radcliff, Jeffrey Chudik, Katrina Molina, Alex Hirsch, Brennon Morning, Evan Pulliam, Stephen Elliott, Ph.D.

Department of Technology Leadership and InnovationPurdue University

West Lafayette, Indiana, USA

B

Int'l Conf. Security and Management | SAM'16 | 223

ISBN: 1-60132-445-6, CSREA Press ©

methodologies for measuring performance are DET curves and the Zoo Menagerie [6, 7].

DET (Detection Error Tradeoff) curves examine the system as a whole, and lower FAR and FRR are the markers of an effective system [3, 7]. Normally DET curves are not overlaid, but for comparison in this study, the curves for specific fingers have been overlaid with different force levels. Performance criteria were measured by False Accept Rate (FAR) and False Reject Rate (FRR) [6]. The performance of each finger, at each force level, are indicated in Figures 5 through 12. On a good curve, as the curve progresses, the number of False Rejects (those genuine matches that are not accepted) should decrease, and the number of False Accepts (those imposter matches that are accepted) should also decrease.

Figure 7 is a good example of a DET curve that clearly indicates better performance of the system using a particular force level for the left middle finger. Looking at the graph, 9N performs better than any other force level, while 5N performs the poorest until 7N overtakes it. The EER (Equal Error Rate) is also an indicator of the accuracy of the system in regards to the algorithm used to run the matching program. Figure 6 has awide range of EER scores, indicating that the system for this finger is not as accurate as it should be for the left little finger.

D. Zoo MenagerieUsers of a biometric system have differing degrees of

accuracy within the system [7]. Doddington's Zoo was the traditional way of categorizing samples based on verification performance when users matched against themselves and with others [8, 9]. In Doddington’s Zoo, the hardest sample to verify was named the goat that did not match well against itself; while the wolf could match well against others (especially lambs), lambs match against themselves, but they also match well with others, making them vulnerable to impersonation [10] [11].

Instead of sheep/goats/lambs/wolves, an additional way to categorize users is to use doves/worms/chameleons/phantoms. These animals are part of Yager and Dunstone's menagerie, which is defined regarding a relationship between the genuine and imposter match scores [10]. This method is more concerned with the dispersion of samples, and whether there are more or less of a particular animal than expected [7]. Yager and Dunstone's animals are mapped on a grid with four colored corners depicting where the animal areas are; there is a top 25%, a bottom 25% and then a combination of the two. Samples are placed in these areas based on statistical performance scores. Those that perform high in both imposter and genuine are chameleons; low imposter and high genuine are doves; high imposter/low genuine are worms; lastly low imposter/low genuine are phantoms. Each animal type should contain approximately 1/16th of the total user population [7].

The performance of a system is important to take into consideration when using a particular method. The zoo plot is a different type of performance indicator than the traditional DET or ROC curve. For a zoo plot representing a population of samples, researchers would want their plot to show that there is the expected 1/16th user population in each area of the graph

[8]. In using the zoo plot as a means of analyzing the performance of the system or the population, the researcher need only to consider the skew of the data. If there are a larger than expected number of results in one corner or another, the raw data and images may need to be evaluated for quality and uniformity to be sure there were no methodic issues with the study. The zoo plot is more concerned with the performance of individuals and uses match scores to show performance, rather than with the performance of the population.

III. METHODOLOGY

The fingerprint samples analyzed in this study were takenfrom a previous collection study done in 2010 for the Department of Homeland Security. The study was done by the International Center for Biometric Research (ICBR). ICBR collected data on 154 subjects, and each subject submitted fingerprints at different force levels. The force levels chosen (5 N, 7 N, 9 N, 11 N, and 13 N) were applied on each user's fingerprints using a 10-print device.

Demographic information such as age, ethnicity, and gender were collected from all subjects. The data was categorized by finger, and each finger further subcategorized by force level.

A. CalculationThe fingers used in this experiment were right index, right

middle, right ring, right little, left index, left middle, left the ring, left little. To determine the optimal force level of the device, some data including the False Acceptance Rate (FAR), False Rejection Rate (FRR), genuine scores, and imposter scores were analyzed through commercially available biometric matching algorithms.

The analyzed data was then visualized with zoo plots and DET curves with Oxford Wave Research Bio-Metrics 1.5 visualization rendering software. It is important to pay special attention to this data to ensure an efficient system is in place. After testing, the overall quality and efficiency of the system are evaluated, and changes are often made to optimize further the system.

IV. RESULTS

A. DemographicsThe study conducted contained 154 individuals. As you can

see from the Figure 1 below, which depicts the range of ethnicities within the study, it is clear that the vast majority of the subjects were Caucasian.

224 Int'l Conf. Security and Management | SAM'16 |


Figure 1. Bar chart of subject ethnicity.

Figure 2. Histogram of subject age.

Figure 2, above, depicts the age range of the subjects in the study. The average of which is a little more than 29 years of age. The majority of subjects, however, were between 20 and 25 years of age. Age data is typically recorded to add context to lower performing fingerprints of older subjects [7].

Figure 3. Pie chart of subject gender.

Although the age and ethnicity of all the subjects were heavily skewed towards one direction, Figure 3, above, shows the gender distribution within the study. It was split nearly evenly with a slight majority of subjects being male.

B. DET Cures and Equal Error RatesTable 1 shows the performance results of the fingerprint

matching analysis. FRR is shown in logarithmic intervals with FAR values of 0.01, 0.1, and 1. Figures 4 through 14and the FRR interval calculations were generated via Oxford Wave.

EER was also recorded for all results. It is important to note that some of the DET curve provided could not display all results due to a logarithmic scale. Figures 6 and 7 only shows four force levels overlaid on a single axis. This was because the error rates for those force levels, 13N on Left Middle and 9N on Left Ring, were too low to appear.

Based on error rates alone, we can see that lower force captures do not perform as well as higher force captures. Index fingers showed lower error rates than the other fingers, with little fingers having much higher error rates.

TABLE 1PERFORMANCE ANALYSIS

Finger Force (N) 0.01 0.1 1 EERLI 13 0.87% 0.65% 0.43% 0.5963%LL 13 4.98% 4.56% 4.11% 3.5820%LM 13 0.33% 0.00% 0.00% 0.0000%LR 13 0.00% 0.00% 0.00% 1.0688%RI 13 1.30% 1.30% 1.26% 1.0162%RL 13 5.84% 4.75% 3.26% 3.2670%RM 13 0.22% 0.22% 0.22% 0.2077%RR 13 0.87% 0.87% 0.80% 0.7312%LI 11 1.77% 1.73% 1.52% 1.8886%LL 11 5.19% 3.33% 3.03% 2.8311%LM 11 1.30% 1.30% 1.30% 1.0436%LR 11 1.08% 1.08% 1.08% 1.0481%RI 11 0.22% 0.22% 0.16% 0.1917%RL 11 2.89% 2.38% 2.16% 1.6495%RM 11 1.08% 0.87% 0.87% 0.8118%RR 11 1.95% 1.94% 1.73% 1.7373%LI 9 0.87% 0.43% 0.43% 0.3103%LL 9 4.80% 3.68% 2.86% 2.5123%LM 9 0.22% 0.00% 0.00% 0.0113%LR 9 0.00% 0.00% 0.00% 0.0400%RI 9 1.30% 1.30% 1.30% 1.0549%RL 9 4.55% 3.62% 3.08% 2.6698%RM 9 1.52% 0.87% 0.65% 0.7099%RR 9 0.43% 0.22% 0.22% 0.1865%LI 7 1.08% 0.87% 0.43% 0.5642%LL 7 11.43% 8.65% 7.25% 6.5041%LM 7 0.22% 0.22% 0.22% 0.2169%LR 7 0.22% 0.22% 0.22% 0.2047%RI 7 1.30% 1.30% 0.79% 0.8630%RL 7 4.37% 4.11% 3.60% 3.1300%RM 7 1.73% 1.52% 1.25% 1.0648%RR 7 1.30% 1.08% 1.08% 1.1466%LI 5 0.87% 0.65% 0.41% 0.3096%LL 5 11.26% 8.53% 7.14% 6.5117%LM 5 2.60% 2.38% 2.16% 2.3496%LR 5 3.03% 3.03% 3.02% 2.7679%RI 5 2.81% 2.38% 2.38% 2.1228%RL 5 6.49% 6.05% 5.42% 4.3585%RM 5 2.38% 2.38% 1.94% 1.4470%RR 5 3.03% 3.03% 2.60% 2.3102%

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Figure 4. DET Curve of Left Index.

Figure 5. DET Curve of Left Little.

Figure 6. DET Curve of Left Middle.

Figure 7. DET Curve of Left Ring.

c

Figure 8. DET Curve of Right Index.

Figure 9. DET Curve of Right Little.



Figure 10. DET Curve of Right Middle.

Figure 11. DET Curve of Right Ring.

C. Zoo PlotsTABLE 2

ZOO ANALYSIS RESULTS

Finger Force(N) Doves Worms Chameleons Phantoms Normals

LI 13 6 5 12 16 115LL 13 6 3 15 12 118LM 13 8 2 13 13 118LR 13 6 5 18 12 113RI 13 8 11 12 10 112RL 13 4 3 18 9 120RM 13 6 5 16 11 116RR 13 6 9 13 10 116LI 11 7 2 10 15 120LL 11 6 2 16 14 116LM 11 9 4 14 16 111LR 11 5 1 13 18 117RI 11 8 3 12 14 116RL 11 7 3 19 10 115RM 11 7 5 14 16 112RR 11 6 2 14 20 112LI 9 7 6 11 11 119LL 9 6 6 12 9 121LM 9 8 3 15 16 112LR 9 7 4 14 9 119RI 9 9 5 18 14 107RL 9 8 5 12 10 119RM 9 7 6 15 16 110RR 9 7 6 15 12 114LI 7 8 6 12 14 114LL 7 5 4 14 11 120LM 7 4 5 13 13 119LR 7 7 4 14 9 119RI 7 5 5 15 15 113RL 7 4 5 18 12 115RM 7 7 7 14 13 113RR 7 9 4 17 14 110LI 5 4 4 19 16 111LL 5 4 4 19 16 111LM 5 6 7 13 14 114LR 5 5 8 16 13 112RI 5 7 11 10 10 115RL 5 5 5 14 14 116RM 5 8 9 12 5 120RR 5 5 9 11 13 117

V. CONCLUSIONS

With the increasing use of biometric technologies in all industries, it is important to understand how different modalities can perform. Knowing how populations perform under different conditions is key to selecting proper testing parameters for maximum success with any population. Our study of fingerprints across multiple force levels allowed us to see clearly the movement of identical samples across different performance indicators and metrics.

A. Zoo Analysis and Animal MovementsExamining the zoo plot results for the left little finger,

there is a movement of 10 samples from the "normal" range into the various animal areas due to the decrease in pressure from 9 N (at best performance) to 5 N (worst performance). Not only was there noticeable movement from normal to the chameleon and phantom areas, but even two of the doves move to a less favorable category due to this decrease in pressure. Increasing the pressure from 9 N to 13 N does not impact the doves’



category, but there is a small movement in all the other categories, most notably the worms, moving three samples to other categories through the changes in pressure.

Figure 12. Zoo Plot of Left Little 9N, highest performance.

Figure 13. Zoo Plot of Left Little 5N, lowest performance.

Figure 14. Zoo Plot of Left Little 13N, with little change in animal distribution since 9N.

B. Analysis of DET CurvesIn considering the DET curve for the left little finger

against the other fingers sampled, the performance of this system for the left little finger performs poorly across all force levels, indicating that this system is a poor choice for identification of this population using this finger. As a standardapplication of this system, it is important to control important variables in regards to what the administrator needs to screen their users, and to consider the system itself as a possible source of variability.

In the analysis of data, the zoo plot allows for quick visual inspection of results, and if more than one study is conducted,the variables and their effects on the samples can be more easily compared to each other if all other variables are controlled. Likewise, the DET curve allows for quick review of system performance in the case of a skewed set.

In real-world implementation and application of this and other systems, it is important to remember that biometric systems are not perfect. As seen from the movement of samples throughout the different force levels, there are consistency issues that have not yet been addressed.

C. Ongoing ResearchMoving forward it will be important to look into why

samples change and how this can be controlled or taken intoaccount to better construct studies for known populations.

Our study was based on a narrow range of ages and ethnicities, to improve the systems used and gain more information, it will be necessary to widen the sample population with follow-on studies to include more diversity.

Additionally, habituation to the collection device and process, as well as the quality of the images collected should also be analyzed. Over time performance of the sample population may increase because placement of the finger and pressure level achievement is easier, and the distortion of the three-dimensional finger in the two-dimensional image will become more uniform. This may not be true of an unhabituated population

VI. REFERENCES

[1] A. K. Jain, A. Ross and S. Prabhakar, "An introduction to biometric recognition," Circuits and Systems for Video Technology, IEEE Transactions on, vol. 14, no. 1, pp. 4-20, 30 January 2004.

[2] K. O'Connor, S. Elliott, M. Sutton and M. Dyrenfurth, "Stability of Individuals in a Fingerprint System across Force Levels – An Introduction to the Stability Score Index," in The 10th International Conference on Information Technology and Applications, 2015.

[3] A. K. Jain, A. A. Ross and K. Nandakumar, Introduction to Biometrics, New York, New York: Springer Science & Business Media, 2014.

[4] L. Hong and A. Jain, "Classification of fingerprint images," in Proceedings of the Scandinavian Conference on Image Analysis, 1999.



[5] D. Maltoni, D. Maio, A. K. Jain and S. Prabhakar, Hanbook of Fingerprint Recognition, Springer Science & Busniess Media, 2009.

[6] T. Dunstone and N. Yager, Biometric system and data analysis: Design, evaluation, and data mining, Eveleigh, New South Wales: Springer Science & Business Media, 2008.

[7] N. Yager and T. Dunstone, "The Biometric Menagerie," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 32, no. 2, pp. 220 - 230, 2010.

[8] M. E. Schuckers, Computational Methods in Biometric Authentication, M. Jordan, R. Nowak and B. Schölkopf, Eds., New York: Springer Science & Business Media, 2012.

[9] A. Martin, G. Doddington, T. Kamm, M. Ordowski and M. Przybocki, "The DET Curve in Assessment of Detection Task Performance," National Institute of Standards and Technology, Gaithersburg, 1997.

[10] T. Dunstone and N. Yager, "Worms, Chameleons, Phantoms and Doves: New Additions to the Biometric Menagerie," in Automatic Identification Advanced Technologies, 2007 IEEE Workshop on, Alghero, 2007.

[11] G. Doddington, W. Liggett, A. Martin, M. Przybocki and D. Reynolds, "Sheep, Goats, Lambs and Wolves: A Statistical Analysis of Speaker Performance in the NIST 1998 Speaker Recognition Evaluation," National Institute of Standards and Technology (NIST), Gaithersburg, MD, 1998.



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