(2007) The impact of fingerprint force on image quality and the detection of minutiae

IEEE EIT 2007 Proceedings 432
.

The Impact of Fingerprint Force on Image
Quality and the Detection of Minutiae
Eric Kukula, Stephen Elliott, Hakil Kim, and Cristina San Martin
Of primary importance during selection of an authentication
Abstract—It is well documented that many factors affect system and whether or not to implement a biometric system is
fingerprint image quality such as age, ethnicity, moisture, to first understand how the target population will react to
temperature and force, although force has only been subjectively biometric technologies, determine what issues might arise, and
measured in the literature. Fingerprint image quality is of utmost
importance due to its linear relationship with matching
understand who your users are and their knowledge,
performance. Therefore, the purpose of this research is to show perception, and anxiousness with using technology. Other
how fingerprint force impacts image quality and the number of questions to consider include how factors such as temperature,
detected minutiae. Two experiments are presented in this paper illumination, noise, etc… affect the performance of the
that evaluated fingerprint force levels and the impacts on image biometric system.
quality, number of minutiae detected, and user comfort to provide Understanding the design of the authentication system and
the community with a quantitative measure for force as it relates
to image quality. Four force levels (3, 9, 15, and 21 newtons) were
the biometric sub-system (and the interaction of the individual
evaluated in the first experiment with results indicating that there and the biometric sensor) is critical, as it must accommodate as
was no incremental benefit in terms of image quality when using many of the intended users as possible, and work in the targeted
more than 9N when interacting with an optical fingerprint sensor. environment. Once these factors have been taken into
The second experiment investigated the 3-9N interval with results consideration, the last step in system evaluation is to evaluate
indicating that the optimal image quality is arrived between a whether the biometric systems expected performance will
force level is 5-7N.
ultimately satisfy the intended purpose for not only the
Index Terms— fingerprint, image quality, biometrics application, but also the users. According to Jain, Pankanti, et
al., the complexity of designing a biometric system is based on
I. INTRODUCTION three main attributes – accuracy, scale (size of the database),
and usability [2]. As utilization of biometric technology
B IOMETRIC technology is defined as the automated
recognition of behavioral and physiological characteristics
of an individual [1]. When deciding whether to implement an
becomes more pervasive, understanding the interaction
between the human, the environment, and the biometric sensor
becomes increasingly imperative.
authentication system, many considerations have to be
Fingerprint recognition is used in a number of wide ranging
examined in order to assess whether incorporating biometric
applications including law enforcement (AFIS), access control,
technologies is suitable. For example, should an access control
time and attendance, and logical access. Fingerprint recognition
system utilize a magnetic lock, a touchpad, or a biometric? A
has an extensive history, but it was not until the late nineteenth
non-exhaustive list of factors that would likely influence this
century that the modern fingerprint classification system was
decision includes the intended users, the environment, the
proposed by Francis Galton and Edward Henry, first as
application, and the design of the system/device. The success of
independent classifications and subsequently as a singular,
biometric technology relies closely on the sensors ability to
comprehensive system. Galton’s contribution to the
collect and extract those characteristics from a vast pool of
comprehensive classification system focused on minutiae
individuals.
points, or singular points of interest that are caused by a change
in the ridge of the fingerprint. Two of the more common types
of minutiae are ridge endings and ridge bifurcations, or forks.
Eric P. Kukula is a graduate researcher in the Biometrics Standards, Part of the definition of biometric systems is that they are
Performance, & Assurance Laboratory, in the Department of Industrial
Technology, Purdue University, 401 N. Grant Street, West Lafayette, Indiana
automatic; with regard to fingerprint identification, users
47907.USA (phone: 765-494-1101; fax: 765-496-2700; e-mail: kukula@ present their fingerprints to a sensor. Of the five common
purdue.edu). URL: http://www.biotown.purdue.edu/research/ergonomics.asp. families of fingerprint sensors (optical, capacitance, thermal,
Stephen J. Elliott is Director of the Biometrics Standards, Performance, &
Assurance Laboratory and Associate Professor in the Department of Industrial
ultrasound, and touchless), the two most widely used are optical
Technology, Purdue University, 401 N. Grant Street, West Lafayette, Indiana and capacitance. Optical sensors are more commonly used in
47907.USA (e-mail: elliott@ purdue.edu). law enforcement, border control, and desktop authentication
Hakil Kim is a Professor in the School of Information & Communication applications, whereas capacitance sensors are found in laptops,
Engineering at Inha University and a member of Biometrics Engineering
Research Center (BERC) at Yonsei University, 253 Yonghyun-dong, Nam-gu, cellular phones, personal data assistants (PDAs), and flash
Incheon, Korea 402-751 (e-mail: hikim@ inha.ac.kr). drives. There is some degree of overlap between capacitance
Cristina San Martin is a graduate student in the Department of Computer and sensors and optical sensors, particularly in access control and
Information Technology, Purdue University, 401 N. Grant Street, West
Lafayette, Indiana 47907.USA (e-mail: csanmart@ purdue.edu). desktop security applications. Fingerprints are matched either
1-4244-0941-1/07/$25.00 c 2007 IEEE

Authorized licensed use limited to: Purdue University. Downloaded on February 27,2010 at 11:03:27 EST from IEEE Xplore. Restrictions apply.

.

through pattern or minutiae extraction. The minutiae-based III. METHODOLOGY
method is typically used by optical sensors (due to the image Two experiments were performed with a CrossMatch
size), whereas the pattern-based method has been developed for VerifierTM 300 LC single optical fingerprint capture device to
the smaller area sensors found on consumer devices. measure the impact fingerprint force has on image quality. The
sensor had the following properties: resolution – 500 DPI ±1%,
II. BACKGROUND
image size (pixels) – 640*480, platen size (inches) – 1.2*1.2,
A. Motivation for this Research and operating temperature – 0°F to 104°F. This sensor was
The motivation for this research was to determine if the force chosen due to its widespread deployment in over 5,000
(pressure) an individual applies to an optical fingerprint sensor applications, some of which include: national ID/registration
can be correlated with the resulting image quality. Kang, et al. programs, border and port entry/exit control, and Child ID
examined finger force and indicated force does impact quality, programs [11]. To measure the force placed on the fingerprint
but did not specify quantitative measures, rather classified force sensor a Vernier Dual-Range Force Sensor was used. The force
as low (softly pressing), middle (normally pressing), and high sensor had a range of ±50N and error of ±0.05N. Fig. 1 shows
(strongly pressing) [3]. Thus, the purpose of this research is to the experimental setup.
quantitatively analyze the impact of fingerprint pressing force
on both image quality and the number of detected minutiae on
fingerprint image quality. This is of importance as image
quality effects the biometric matching algorithm as discussed in
[4-7].
B. Influence of the Scientific Discipline of Human Factors
and Human-Computer Interaction on Biometrics
Historically the biometrics community has performed
limited work in the area of human-computer interaction and
related fields of ergonomics and usability. Recent work
conducted by the National Institute of Standards and Fig. 1: Experimental setup showing the optical fingerprint sensor and force
Technology (NIST) examined the impact different heights of a sensor.
fingerprint sensor have on image quality and capture time. The
study also examined user preferences to particular heights [8]. Both experimental procedures required participants to use
Results from the study consisting of 75 NIST employees their right index finger to reduce some variability in
revealed a counter height of 36 inches (914 mm) gives fastest measurement in terms of dexterity and finger size, but could not
performance, while the 26 inch (660 mm) counter height gave account for all variability between people. In cases of extreme
the highest quality fingerprint images, and a counter height of scarring or other irregularities, the left index finger or middle
32 or 36 inches (813 or 914 mm) was the most comfortable for fingers were used. Three fingerprint images were collected and
users [8]. stored for each force level used. Fingerprint capture was
Similar work has been conducted by Kukula, Elliott, et. al performed by the test administrator, which grabbed the
with hand geometry to investigate the effect that different fingerprint image when the force level (f) was within the set
working heights have on the performance of a hand geometry tolerance of f ± 0.50N for the first experiment and f ± 0.25N for
device [9]. Four hand geometry devices were used in this the second. The precision was increased for the second
evaluation with the heights at which devices were placed at the experiment due to the measured force increments reducing
three recommended height ranges provided by Grandjean for from 6N to 2N. Experiment 1 used four force levels: 3N, 9N,
the different types of work: precision, light, and heavy [10]. 15N, and 21N, where as experiment 2 used five force levels:
The fourth device was mounted at a similar height as the hand 3N, 5N, 7N, 9N, and 11N. The order of image collection
geometry device that was found on the Purdue BSPA followed the same procedure for all participants which went in
laboratory door. The results of the 32 participant evaluation increasing order of force levels. Since image quality was the
revealed that there was no difference among the median value dependent variable, the platen was cleaned with a micro fiber
of match scores across the different four heights (30, 35, 40, towel between each finger placement to ensure sweat and oil
and 45 inches), thus allowing the biometric device installer residue was not on the platen. After the completion of each
some flexibility when implementing hand geometry devices. force level, the participants answered a usability question based
Users were also surveyed to establish which device satisfied on comfort levels, which are shown in Fig. 2. Data collection
them. The results showed that 63% of the users preferred the was performed with all subjects seated on a stool at a height
hand geometry device mounted at 40 inches. In addition, the above the sensor, to minimize stressors on the body.
study revealed a correlation of preferred hand geometry height
and that of the user; therefore a practitioner should take into
consideration the height of the intended user audience before
installing a hand geometry device.


.

B. Within Experiment Analysis
" !!!# !!!$
=1 =2 =3 To analyze the results of each experiment multiple Analysis
CIRCLE THE FACE THAT BEST DESCRIBES of Variance (ANOVA) tests were performed. ANOVA tests are
YOUR COMFORT AT THIS LEVEL an instrument to compare the effect of multiple levels of one
factor (force) on a response variable (image quality, number of
minutiae), which is a generalization of the two-sample t-test.
level.!
Fig. 2: Usability metric of user comfort asked after interaction at each force
The ANOVA is partitioned into two segments: the variation
that is explained by the model (2) and the variation not
Once the fingerprint samples were collected, the prints were explained, or error (3), which are both used to calculate the
analyzed with Aware Wavelet Scalar Quantization (WSQ) F-statistic (4) testing the hypothesis Ho: 1 = 2 = … = I and
VBQuality software v2.42E. The following variables were Ha: not all ’s are the same. In practice p values are used, but
reported by the software: quality score, minutiae, and the the Fobserved test statistic can also be compared to the F
number of core(s)/delta(s). The image quality score ranged distribution table as shown in (5). Typically when the Ho is
from 0-99, with zero being a bad quality image score and 99 rejected the variation of the model (SSM) tends to be larger
being the best quality score. than the error (SSE), which corresponds to a larger F value.

IV. EXPERIMENTAL DESIGN
( )
SSM = ' Yi − Y , dfM = 1, MSM = SSM dfM
ˆ 2
(2)

SSE = ' (Y − Y ) , dfE = n − 2, MSE = SSE dfE
A. Between Experiment Analysis 2
In order to compare results from the two experiments, the
ˆ i i (3)
experimental design was created in such a way that two control F = MSM MSE ~ F (dfM , dfE ) (4)
groups (force levels of 3N and 9N) were the same in both
experiments, except for the reduced tolerance difference in F ≥ F (1 − α , dfM , dfE ) (5)
experiment two. Note the graphs for 3N and 9N are colored
differently in Figs. 3 – 6 for ease in comparing. Two-sample V. EXPERIMENT ONE
t-tests were performed on the 3N and 9N image quality scores
and minutiae counts to examine if there were differences A. Specific Procedures
between the two studies. The equation for the two-sample t-test Experiment 1 consisted of 29 participants between the age of
can be found in (1), with hypothesis Ho: µ1 = µ2 and Ha µ1 ! µ2. 18 and 25 which took place in October of 2006. In this study, 2
individuals had scarring or other irregularities, of which one
used the right middle, and one used the left index finger. Four
& s12 # & s 2
2
#
T = (Y 1 − Y 2 ) $ N !+$ N ! (1) force levels were evaluated: 3N, 9N, 15N, and 21N. Fingerprint
% 1" % 2" images for each of the corresponding force levels can be seen in
Fig. 3.
The two sample t-test of image quality score by experiment for
force level 3N revealed that there were no differences in the
means, t (0.975, 182) = -1.37, p = 0.171. The 9N two-sample t-test
revealed there were also no significant differences in the means
of image quality scores between the two experiments, t(0.975, 192)
= 0.83, p = 0.406.
While the image quality scores were similar between the two 3N Force 9N Force 15N Force 21N Force
Quality 53 Quality 60 Quality 74 Quality 84
experiments, the numbers of minutiae between the two
experiments at the 3N and 9N force levels were statistically
Fig. 3: Fingerprint images for Experiment One by force level with reported
significant, t(0.975, 197) = -3.27, p = 0.001 and t(0.975, 213) = -3.39, p image quality score.
= 0.001, for 3N and 9N respectively.
Thus, the results of the t-tests indicate that the two test B. Statistical Analysis and Results
populations used in the two experiments are similar in terms of The results of Experiment One’s ANOVA for image quality
overall image quality scores, but differ in minutiae, which may score revealed that there was a statistically significant
be attributable to test crew composition (ethnicity, gender, difference between image quality scores and the four force
finger moisture) and external environmental conditions levels (3N, 9N, 15N, and 21N) applied to the sensor, F(.95, 3, 344)
(temperature, humidity) as discussed in [5, 7, 12, 13], but were = 22.56, p = 0.000. The frequency plots of image quality scores
not evaluated in either of the experiments. can be seen in Fig. 4, which is organized by force level. This
plot graphically depicts the ANOVA results – the quality scores
for 3N are more spread than the other three force levels.


.

Fig. 6: Experiment one user reported comfort level by force level.

Fig. 4: Experiment one frequency plot of quality scores by force level.
VI. EXPERIMENT TWO
Investigating the quality scores further, one can reach the
Similar to image quality, the number of minutiae located was conclusion that the scores significantly increased between 3N
more spread at the lower end of the distribution for the 3N force and 9N, but there was minimal benefit of applying more than
level than the other three levels (Fig. 5). The ANOVA results 9N of force, as the quality scores only increased minimally.
confirm this, as there was a statistically significant difference Thus, we investigated the 3N – 9N interval in experiment two.
between number of minutiae and force level, F (.95, 3, 344) = 30.69, A. Specific Procedures
p = 0.000.
Experiment 2 consisted of 43 participants aged between
18-25 years old and took place in January of 2007. The subjects
were unique to each test. All participants used their right index
finger. The five force levels investigated were: 3N, 5N, 7N, 9N,
and 11N. Fingerprint images for one user at each of the
corresponding force levels can be seen in Fig. 7.

3N Force 5N Force 7N Force 9N Force 11N Force
Quality 3 Quality 87 Quality 91 Quality 88 Quality 90

Fig. 7: Fingerprint images for Experiment Two by force level with reported
image quality score.

Fig. 5: Experiment one frequency plot of minutiae count by force level. B. Statistical Analysis and Results
The results of Experiment Two’s ANOVA for image quality
score revealed that there was a statistically significant
The subjective comfort level question also revealed that the difference between image quality scores and the five force
more pressure a user applied, the more uncomfortable it is for levels (3N, 5N, 7N, 9N, and 11N) applied to the sensor, F(.95, 4,
subjects to interact with the device (Fig. 6). 640) = 6.88, p = 0.000. However, as expected the value of the F
statistic is lower for this model, which is attributable to the
smaller force level increments (2N between levels as opposed
to 6N in Experiment 1) under investigation. The frequency
plots of image quality scores can be observed in Fig. 8, which is
organized by force level. This plot graphically depicts the
ANOVA results – the quality scores for 3N are more spread
than the other three force levels which are heavily skewed to the
left.


.

Fig. 10: Experiment two user reported comfort level by force level.

VII. CONCLUSION
The purpose of this study is to quantitatively analyze the
impact of fingerprint pressing force on both image quality and
the number of detected minutiae. Investigating the quality
scores of experiment two further, one can deduce that the
Fig. 8: Experiment two frequency plot of quality scores by force level.
quality scores significantly increase between the 3N and 5N-7N
Since the force level intervals were smaller, there was more force level, and actually regressed at 11N, but there was
of an overlap in the number of minutiae located; however, the minimal benefit of applying more than 9N of force, as the
number was denser at lower end of the distribution for the 3N quality scores did not improve by much, plus were deemed as
force level than the other four levels (Fig. 9). The ANOVA neutral or unsatisfactory by the users. Moreover, it is apparent
results revealed a statistically significant difference between from these two experiments, the users were less comfortable
number of minutiae and force level, F(.95, 4, 640) = 19.52, p = using the fingerprint device when more force had to be applied
0.000, which like the image quality for experiment two, the F to the sensor with their finger. Thus, the recommended force
statistic was lower than for experiment one due to the smaller level an individual should apply to an optical sensor such as the
intervals. one used in these experiments should be approximately five to
seven newtons, which is approximately two newtons more than
what an average person applies when typing on a computer
keyboard (3-5 newtons).

REFERENCES
[1] International Organization for Standardization, "ISO/IEC
JTC1/SC37 Standing Document 2 - Harmonized Biometric
Vocabulary," WD 2.56 ed: ANSI, 2007, pp. 66.
[2] A. Jain, S. Pankanti, S. Prabhakar, L. Hong, and A. Ross,
"Biometrics: A Grand Challenge," presented at 17th International
Conference on Pattern Recognition (ICPR 2004), Guildford, UK,
2004.
[3] K. Kang, B. Lee, H. Kim, D. Shin, and J. Kim, "A Study on
Performance Evaluation of Fingerprint Sensors " in Audio- and
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Berlin / Heidelberg: Springer 2003, pp. 574-583.
[4] A. Jain, Y. Chen, and S. Dass, "Fingerprint Quality Indices for
Predicting Authentication Performance," presented at 5th
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Authentication, Rye Brook, NY, 2005.
Fig. 9: Experiment two frequency plot of minutiae count by force level. [5] S. K. Modi and S. J. Elliott, "Impact of Image Quality on
Performance: Comparison of Young and Elderly Fingerprints,"
The subjective comfort level question for experiment two presented at 6th International Conference on Recent Advances in
Soft Computing (RASC), Canterbury, UK, 2006.
was interesting, as participants were very neutral to applying 7 [6] E. Tabassi and C. L. Wilson, "A novel approach to fingerprint image
or 9 newtons of force to the sensor, where as in Experiment one quality," presented at International Conference on Image
most participants were comfortable with applying 9N of force. Processing, Genoa, Italy, 2005.
[7] M. Yao, S. Pankanti, and N. Haas, "Fingerprint Quality
Overall, the frequencies for experiment two (Fig. 10) correlate Assessment," in Automatic Fingerprint Recognition Systems, N.
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[8] M. Theofanos, S. Orandi, R. Micheals, B. Stanton, and N. Zhang,
"Effects of Scanner Height on Fingerprint Capture," National
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[9] E. Kukula, S. Elliott, P. Senarith, and S. Tamer, "Biometrics and
Manufacturing: A Recommendation of Working Height to
Optimize Performance of a Hand Geometry Machine," Purdue
University Biometrics Standards, Performance, & Assurance
Laboratory, 2007, pp. 18.
[10] E. Grandjean, Fitting the Task to the Man: A Textbook of
Occupational Ergonomics, 4 ed. London: Taylor & Francis, 1988.
[11] CrossMatch Technologies, "Verifier 300 LC 2.0 Single Finger
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[12] International Organization for Standardization, "ISO/IEC
JTC1/SC37 - Information Technology – Biometric Performance
Testing and Reporting – Part 3: Technical Report on
Modality-Specific Testing ": ANSI, 2007, pp. 27.
[13] N. Sickler and S. Elliott, "An evaluation of fingerprint image quality
across an elderly population vis-a-vis an 18-25 year old population,"
presented at 39th Annual International Carnahan Conference on
Security Technology (ICCST), Las Palmas de Gran Canaria, Spain,
2005.


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(2007) The impact of fingerprint force on image quality and the detection of minutiae