This document studied the impact of different levels of applied force on the quality of fingerprint images captured by an optical fingerprint sensor. It found that applying a force between 5-7 Newtons when capturing fingerprints generally produced higher quality images and a greater number of detected minutiae compared to letting the sensor automatically capture fingerprints without a specified force level. However, matching performance was slightly better for automatic captures compared to forced captures between 5-7 Newtons. The document concludes that the optimal force level may vary between different optical fingerprint sensors.

1. The Impact of Force on Fingerprint Image Quality,
Minutiae Count and Performance
Michael Petrelli, Stephen Elliott, Carl Dunkelberger
Department of Technology, Leadership and Innovation
Purdue University
West Lafayette, IN 47907
Abstract— The impact of force on acquiring a high quality
fingerprint image has been systematically studied by researchers
using single print optical scanners. A previous study examined
force levels that ranged from 3N to 21N using a single print
optical sensor. A second experiment used force levels ranging
from 3N to 11N using a capacitive and optical single print sensor.
Additional work has been conducted that looked at smaller
increments of force also using an optical sensor. This paper
contributes to the body of knowledge by using an alternative
fingerprint sensor, an alternative force level and compares it to
the auto-capture method.
Keywords-fingerprint, image quality
I. INTRODUCTION
In fingerprint recognition systems, the quality of an acquired
fingerprint has a significant impact on overall system
performance. There are a number of factors that contribute to
the performance of a biometric system. These factors can come
from a number of different sources, whether that be from the
subject, the test administrator, or the system [1]. One of the
greatest limitations in acquiring quality images from fingerprint
devices involves both inconsistent surface contact and non-
uniform contact, between the finger and device platen [2].
Placement variables such as horizontal and vertical translation,
rotation around the scanner, roll and orthogonal placement,
torque and applied force can be manipulated at the moment of
interaction [3-6]. The manipulation of the amount of force the
subject places on the platen should reduce these distortions and
therefore provide more consistent images from the subject.
There has been some research on this topic, for example [7]
conducted two experiments; the first using four force levels
(3N, 9N, 15N, and 21N) and the second using five force levels
(3N, 5N, 7N, 9N, and 11N). In that research, the recommended
force levels that an individual should apply to a single print
optical sensor would be approximately 5N – 7N. Another study
[8] used a different optical sensor and chose force levels
ranging from 1.5N to 7.5N in increments of ± 0.5N. In that
study 5.5N was identified as an optimal force level for the
performance of that particular sensor. Both of these studies
used the index finger. The motivation behind this paper is to
examine the performance of a different optical fingerprint
sensor, as well as to see how force impacts image quality and
minutiae distribution across different fingers.
II. METHODOLOGY
A commercially available 10-print fingerprint scanner was
placed with its base 39 inches off of the ground, which was
selected because that was the most common height of the table
where this type of scanner is used. The recommendations of [9]
were also considered in the design of the study. Subjects were
required to stand during the fingerprint capture process. Once
the subjects had completed the necessary consent procedures,
they completed the various demographic questions (see Table
1), followed by a demonstration of the process by the test
administrator. The subject was allowed to practice with the
sensor. A number of variables were of interest, including the
image quality score, minutiae count, time to complete the
process, False Reject Rate at 0.1%, 0.01%, and 0.001% FAR
levels, and failures from either an acquisition or extraction of
the image.
The experiment consisted of two fingerprint capture methods
referred to as auto-capture and force-capture. Three images
were collected from the right and left index, middle, and
thumbs for both the auto-capture and force method. The first
method utilized the CrossMatch Guardian L Scan 500’s
proprietary auto-capture system to collect images. This method
is referred to as auto-capture. In this method, individuals were
not allowed to see their image or given feedback on how hard
they were pressing. The second method captured images based
on how hard the individual was pressing on the platen. The
capture event occurred only after a fingerprint was detected on
the platen within the force range of between 5N – 7N. This
method is referred to as force-capture. Due to the nature of the
setup, all subjects completed the auto-capture data collection
before moving onto the force level study. The subject was
allowed to see how hard they were pressing and the feedback
was provided on a large textbook indicating applied force in
Newton’s. As in the auto-capture method, the subject was not
able to see their image, or given feedback apart from the force
display. A timeout occurred if the subject took more than 30
seconds. The subjects had to disengage from the sensor after
every interaction. After the subject had completed the auto-
capture data collection, they were asked to complete a survey
that asked them about the comfort level of the device. The
subject then returned to the data collection process and
completed the force component.
978-1-4577-0490-1/11/$26.00 ©2011 IEEE

2. The data collection system was composed of a platform, force
sensors, USB Analog and Digital Input/Output (I/O) module,
fingerprinting scanner, personal computer and user interface.
The platform consisted of a 6.5”x6.5”x0.25” piece of
compressed wood to provide an area to place the fingerprint
scanner and force sensors. The force sensors consisted of
Honeywell FSS1500 High Precisions Force Sensors located at
each of the four corners of the platform. These force sensors
were both powered and measured by Measurement
Computing’s USB-based Analog and Digital I/O Module
USB-1408FS. The fingerprint scanner was a Guardian LScan
500 by CrossMatch Technologies. The I/O module and
fingerprint scanner both connected to a single core 2GHz
personal computer with 2Gb of random access memory.
III. RESULTS AND ANALYSIS
Data was collected from 70 individuals; the breakdown of the
demographic information is shown in Table 1. The majority of
subjects (87%) were college aged, with 91% self-selecting
office worker as their occupation category. Seventy-four
percent of the subjects had previous experience with
fingerprint sensors in general. This was expected, as there
were a number of studies being conducted on fingerprint
sensors during that time. Furthermore, there are several
businesses aimed at the college population that have optical
fingerprint sensors in the local community (e.g. a tanning
salon has a fingerprint sensor installed for identification).
Seventy seven percent of the subjects had not had experience
with this specific optical sensor.
Table 1: Demographic Information
Category Responses Number Percentage
Age 18-29 61 87%
30-39 5 7%
40-49 1 1%
50+ 3 4%
Ethnicity African-
American
1 1%
Asian 25 36%
Hispanic 1 1%
Latino 1 1%
Native
American
1 1%
Caucasian 41 59%
Gender Male 23 33%
Female 47 67%
Labor
Distribution
Manual 6 9%
Office 64 91%
Dominant Hand Left 5 7%
Right 65 93%
Missing Finger
or
Musculoskeletal
condition?
No 69 1%
Yes 1 99%
Prior No 18 26%
Experience with
Single Print
Scanner
Yes 52 74%
Prior
Experience with
Ten Print
Scanner
No 54 77%
Yes 16 23%
1260 images were collected from the right and left index
fingers, middle fingers, and thumbs, and were compared to
1258 images captured using the force-capture method. The
breakdown of these images can be found in Table 2.
TABLE 2: FREQUENCY OF FINGERS ACROSS AUTOCAPTURE (AC) AND FORCE
CAPTURE (FC)
RI RM RT LI LM LT
AC 210 210 210 210 210 210
FC 210 209 210 210 210 209
A. Image quality analysis
The first stage of the analysis was to examine image quality.
As the data was non-normal, a Kruskal-Wallis was used to
determine if there were any significant differences among the
population medians. Whilst the test statistic is a general
hypothesis to examine whether all the population medians are
equal, an effective way of doing pairwise simultaneous
inferences can be completed by using a Dunn’s Test. Figure 1
shows the box plots of the finger image quality fingers (LI,
LM, LT, RI, RM, RT)on the left side of the image. To the right
are the pairwise comparisons, which show the magnitude of the
group differences and their direction. Table 3 illustrates the
groups that showed significant differences (adjusted for ties).
Figure 1a: Dotplots with Sign Confidence Interval

3. Figure 1b: Pairwise Comparisons
TABLE 3: KRUSKAL-WALLIS CONCLUSIONS
Groups Z vs. Critical
Value
P-value
LI vs. LR 4.97192 >= 2.475 0.0000
LR vs. RM 4.41433 >= 2.475 0.0000
LM vs. LR 4.26114 >= 2.475 0.0000
LR vs. RI 4.03135 >= 2.475 0.0001
LI vs. RT 2.97527 >= 2.475 0.0029
The next stage of the analysis was to examine whether there
is any significant difference between auto-capture and force
levels. Again, the data was non-normal, so a Kruskal-Wallis
test was conducted. The sample medians for the two
treatments were calculated as 86 and 87. The test statistic (H)
had a p-value of 0.005, both unadjusted and adjusted for ties,
indicating that the null hypothesis can be rejected at α=0.05.
The higher median score was observed by the force-capture
algorithm. Table 4 shows the Mann-Whitney test results.
TABLE 4: IMAGE QUALITY
Finger (force vs.
autocapture)
p-value
Left Index 0.0065
Left Middle 0.0084
Left Thumb 0.0858
Right Index 0.0173
Right Middle 0.0041
Right Thumb 0.0695
The results in Table 4 show that there are significant
differences in the quality of the image for force and auto-
capture (at α=0.05), except for thumbs. The data from the right
and left thumb do not support the hypothesis that there is a
difference between the population medians.
B. Minutiae analysis
The numbers of minutiae were detected using commercially
available image quality software package. 1260 images from
the auto-capture method were compared to the 1258 images
from the force-capture method. Due to the lack of normality, a
non-parametric test was conducted. Table 5 shows the results of
differences of minutiae across the two force levels.
TABLE 5: MINUTIAE ANALYSIS
Finger (force vs.
autocapture)
p-value
Left Index 0.8269
Left Middle 0.2347
Left Thumb 0.0003
Right Index 0.0075
Right Middle 0.0606
Right Thumb 0.0206
The results here are mixed. The minutiae counts of the left
index, left middle, and right middle do not support the
hypothesis that there is a difference across the medians. Left
thumb, right thumb and right index do support the hypothesis
that there are differences across the medians. Further research
identifying the differences in the minutiae counts is
recommended.
C. Failure to Acquire and Failure to Extract
The next test was to determine whether or not the
proportions of failures to acquire were different between
capture methods. A failure to acquire was defined as “the
expected proportion of transactions for which the system is
unable to capture or locate an image or signal of sufficient
quality” [10]. A failure to acquire occurred when the capture
process took longer than 30 seconds. Failure to locate an
image of sufficient quality is referred to as failures to extract.
The frequencies of these occurrences are shown in Table 6.
TABLE 6: FREQUENCY OF FAILURE OCCURRENCE
Auto-
Capture
% Force-
Capture
%
Failures
to capture
within 30
seconds
0 0% 1 0.08%
Failures
to extract
34 2.70% 48 3.81%
Total 34 2.70% 49 3.89%
The force-capture method observed 15 more failures to
acquire than the auto-capture method with 2.7% failures to
acquire as compared to the 3.89%. This may be because of the
higher tolerance of acceptable images associated with that
algorithm. Because failures to acquire deals with proportions,
a two sample proportion test was completed to compare the

4. success rates between the two capture methods. The results
indicate no statistically significant difference between capture
methods for proportion of failures to enroll with a p-value of
0.092.
D. Process Time
Timer values were logged after each successful capture
event. The start time for the capture process began at the
command to place the finger on the platen and was stopped
when the finger image was captured. Thirty successful capture
log entries were removed from the data set as these subjects did
not follow the instructions on finger placement. Several of the
subject interactions caused long process times. One large spike
of six large process times came from one subject. The results
show that there was a statistically significant difference in the
time required to process an image with a p-value of 0.000.
E. Habituation
Habituation may have affected how quickly an image was
captured between the first, second, and third interactions, as
well as across capture methods. As a subject uses a device,
they may become more familiar with the device as the data
collection procedure progressed. As the subjects processed
through autocapture and then the force process, the issue of
acclimation to the device may compromise the results.
Although the results above showed that there was a significant
difference in the process times across force and autocapture,
there was no difference in process time between fingers. If
habituation was present, then we would expect to see the
process time decrease across each interaction. The process
time between fingers in the autocapture method was p=0.323
and the force method were 0.091. Furthermore, if habituation
was a factor, it might be expected that the number of failure to
acquire would be greater for force as opposed to autocapture.
F. Matching Performance
Matching performance was compared by generating a
Detection Error Tradeoff (DET) curve and testing false accept
rates at 0.1%, 0.01% and 0.001% FAR. The results are shown
in Figure 2, with autocapture denoted by the blue line, and
force capture denoted by the red line.
Figure 2: Detection Error Tradeoff Curves
The FRR comparison at 0.1% FAR is shown below in
TABLE 7: FRR COMPARISON AT 0.1% FAR
Auto-capture FRR Force-capture FRR
FAR 0.1% 0.41% 0.49%
FAR 0.01% 0.41% 0.50%
FAR 0.001% 0.41% 31.6%
The results show that force-capture method has a slightly
worse matching performance compared to the auto-capture
method at the 0.1% and 0.01% FAR. However, at the 0.001%
FAR, the force-capture at 5-7N was worse by a significant
margin.
IV. CONCLUSIONS AND RECOMMENDATIONS
This paper provided results of a commercially available
optical sensor for a single-print. Adding to the body of
knowledge, this particular optical sensor did not provide better
performance at the 5-7N force level, although these differences
at FAR 0.1% and 0.01% were not significant. Furthermore,
additional insight was provided by examining the individual
finger and the impact of force on image quality and minutiae
count. Further research should investigate whether there is any
significant impact in the performance of individual fingers
across force levels in general. An interesting conclusion of this
paper shows that in the case of optical fingerprint sensors, there
are disparities in the optimal level of force; a further study
should examine interoperability of fingerprints across different
optical sensors at different force levels.
REFERENCES
[1] A. Hicklin and R. Khanna, “The role of data quality in biometric
systems,” White Paper. Mitretek Systems (February 2006), no.
February, 2006.
[2] E. Tabassi and C. L. Wilson, “A novel approach to fingerprint
image quality,” in IEEE International Conference on Image
Processing, 2005. ICIP 2005, 2005, vol. 2, pp. 37-40.
[3] Atos Origin, UK Passport Service Biometrics Enrolment Trial.
2005, p. 299.
[4] E. Kukula, S. Elliott, H. Kim, and C. San Martin, “The impact of
fingerprint force on image quality and the detection of minutiae,” in
Electro/Information Technology, 2007 IEEE International
Conference on, 2007, pp. 432–437.
[5] A. K. Jain, A. Ross, and S. Prabhakar, “An introduction to biometric
recognition,” IEEE Transactions on Circuits and Systems for Video
Technology, Special Issue on Image- and Video-Based Biometrics,
vol. 14, no. 1, pp. 4-20, Jan. 2004.
[6] S. K. Modi, S. J. Elliott, J. Whetsone, and H. Kim, “Impact of Age
Groups on Fingerprint Recognition Performance,” in 2007 IEEE
Workshop on Automatic Identification Advanced Technologies,
2007, pp. 19-23.
[7] B. Senjaya, S. J. Elliott, S. K. Modi, and T. B. Lee, “Examination of
Fingerprint Image Quality and Performance on Force Acquisition
vis-a-vis Auto Capture,” in 44th Annual IEEE International
Carnahan Conference on Security Technology, 2010, pp. 237-242.

5. [8] E. Kukula, S. Elliott, and C. San Martin, “The impact of fingerprint
force on image quality and the detection of minutiae,” in 2007 IEEE
International Conference on Electro/Information Technology, 2007,
pp. 432-437.
[9] M. Theofanos, S. Orandi, R. Micheals, B. Stanton, and N. Zhang,
“Effects of Scanner Height on Fingerprint Capture.” National
Institute of Standards and Technology, p. 58, 2006.
[10] A. J. Mansfield and J. L. Wayman, Best Practices in Testing and
Reporting Performance of Biometric Devices ver 2.01. Teddington: ,
2002, pp. 1-36.