SAM9701

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. Biometrics
Biometrics 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 Recognition
A 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 Performance
The 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 Innovation
Purdue 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 a
wide 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 Menagerie
Users 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 taken
from 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. Calculation
The 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. Demographics
The 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 Rates
Table 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 14
and 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 1
PERFORMANCE ANALYSIS
Finger Force (N) 0.01 0.1 1 EER
LI 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%
Unspecified
Indo-Irish
Indian
H
ispanic
/ Latino
Caucasian
Asian
orPacific
Islander
Am
erican
Indian
or Alaska
N
ative
African
Am
erican
120
100
80
60
40
20
0
Count
7
11
5
120
7
1
12
Chart of Ethnicity
645648403224
60
50
40
30
20
10
0
Age
Frequency
1
3
2
45
3
6
43
7
12
49
55
Histogram of Age
Male
52.6%
Female
47.4%
Pie Chart of Gender

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 Plots
TABLE 2
ZOO ANALYSIS RESULTS
Finger
Force
(N)
Doves Worms Chameleons Phantoms Normals
LI 13 6 5 12 16 115
LL 13 6 3 15 12 118
LM 13 8 2 13 13 118
LR 13 6 5 18 12 113
RI 13 8 11 12 10 112
RL 13 4 3 18 9 120
RM 13 6 5 16 11 116
RR 13 6 9 13 10 116
LI 11 7 2 10 15 120
LL 11 6 2 16 14 116
LM 11 9 4 14 16 111
LR 11 5 1 13 18 117
RI 11 8 3 12 14 116
RL 11 7 3 19 10 115
RM 11 7 5 14 16 112
RR 11 6 2 14 20 112
LI 9 7 6 11 11 119
LL 9 6 6 12 9 121
LM 9 8 3 15 16 112
LR 9 7 4 14 9 119
RI 9 9 5 18 14 107
RL 9 8 5 12 10 119
RM 9 7 6 15 16 110
RR 9 7 6 15 12 114
LI 7 8 6 12 14 114
LL 7 5 4 14 11 120
LM 7 4 5 13 13 119
LR 7 7 4 14 9 119
RI 7 5 5 15 15 113
RL 7 4 5 18 12 115
RM 7 7 7 14 13 113
RR 7 9 4 17 14 110
LI 5 4 4 19 16 111
LL 5 4 4 19 16 111
LM 5 6 7 13 14 114
LR 5 5 8 16 13 112
RI 5 7 11 10 10 115
RL 5 5 5 14 14 116
RM 5 8 9 12 5 120
RR 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 Movements
Examining 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 Curves
In 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 standard
application 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 Research
Moving forward it will be important to look into why
samples change and how this can be controlled or taken into
account 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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