(2004) The challenges of the environment and the human/biometric device interaction on biomertric system performance
The Challenges of the Environment and the Human / Biometric Device
Interaction on Biometric System Performance
Stephen J. Elliott, Ph.D., Eric P. Kukula Nathan C. Sickler
Biometric Standards, Performance, and Assurance Laboratory
Department of Industrial Technology,
West Lafayette, IN 47906, US
email@example.com firstname.lastname@example.org email@example.com
Abstract performance, and how the image quality of a
biometric sample changes with age.
This paper outlines various research projects Furthermore, all of these factors may be
that have been conducted at Purdue University compounded by changes in the environment,
in the areas of environment, population, and such as the effects of variances in lighting, or
devices. These areas are of interest as biometric camera placement with respect to face
technologies are currently being implemented in identification . The Face Recognition
various business applications. The Vendor Test (FRVT) assessed if recent
environmental research is concerned with the advancements in facial recognition systems
performance of a facial recognition algorithm at actually improved performance. Ten vendors
differing illumination levels. The second study participated in this test, which concluded that
looks at population, which examines differences variation in outdoor lighting conditions, even
in image quality with regard to population age. with images that were collected on the same day,
The third study outlines dynamic signature drastically reduced system performance.
verification and the issues associated with Specifically, the verification performance for the
signing on different digitizers. best face recognition systems drops from 95% to
54% going from indoors to outdoors . This
poses the fundamental question: if an individual
1. Introduction enrolls at a drivers license bureau, then has their
photograph taken in an environment with
Biometric identification technology is defined controlled illumination for identification
as the “automatic identification or identity purposes, facial recognition systems will verify
verification of (living) individuals based on that individual 95% of the time in that
behavioral and physiological characteristics . environment. However, if the verification
Biometric research centers on five fundamental attempt occurs outdoors, the facial recognition
areas: data collection, signal processing, system performance would fail about 46% of the
decision-making, transmission, and storage. time on the same day, even on the same day as
Each of these areas has specific challenges enrollment, even eliminating template aging as a
associated with them. The research described factor. In most real world scenarios, an
below examines some of the problems associated individual will not be identified in the same
with biometric authentication, namely the environment or on the same day as they enrolled;
environment, population, and devices. These therefore more research is needed in this area to
research areas are fundamental, because the assess system performance in a variety of
widespread adoption of biometric technologies conditions.
requires an understanding of how each of these Second, we have limited knowledge in the
factors can affect the performance of the device. gathering of biometric templates in an aging
Furthermore, it is also significant for both population (62 and older) and how this may
government and industry to have research affect system performance as well as image
disseminated on how biometric samples perform quality. Sickler and Elliott  found in
over time, the resulting effects on system preliminary tests of fingerprinting, that elderly
have poor ridge definition and lower moisture
Low Light - 8 lux Medium Light – 423 lux High Light - 800 lux
Figure 1: Sample Verification Images
content, resulting in drier images that are of light) and from the Department of Industrial
lower quality than younger individuals (18-25 Technology office (high light). The third light
year olds). Again, this is of significance as some level (medium light), was determined by taking
states are implementing fingerprint recognition the mean of the other two light levels. Figure 1
for social services, as well as for drivers licenses. shows sample images illustrating the differences
Third, there is a need to see how biometric between the three illumination levels.
samples change when collected on different
devices. In , over 15,000 dynamic signatures
were collected on different mobile computing Kukula and Elliott completed a study
devices, with the resulting signatures analyzed to that consisted of thirty individuals, of which 73%
see which variables are stable over different were male, between the ages of 20 and 29 and
digitizers. The purpose of this research was to Caucasian. Other represented ethnicities were
test signature verification software on both Hispanic and Asian/Pacific Islander. 30% of the
traditional digitizer tables and on population had facial hair, 24% wore glasses,
wireless/mobile computing devices, in order to and 6% wore hats. Two participants also had
assess how the dynamics of the signature signing problems during enrollment, which was
on such devices change. Specifically, the attributable to the subjects wearing a hat; but
researcher examined the differences on table- when it was removed, enrollment was successful.
based digitizers (Wacom Intuit and the e-Pad), There were 6 enrollment failures out of 96
and over mobile computing devices (Palm IIIxe, attempts. Therefore, the overall failure to enroll
Symbol 1500 and 1740 devices). The conclusion (FTE) rate was 6.25%. The failure to acquire
of the research was that was significant (FTA) rates for low light (7-12 lux), medium
differences in specific variables across different light (407-412 lux), and high light (800-815 lux)
digitizers. Therefore, when deciding on were 0.92%, 0.65%, and 0%, respectively .
implementing dynamic signature verification The statistical analysis revealed that at a high
using different digitizers, the device type and illumination enrollment, the illumination of the
identity needs to be attached within the signature verification attempt was not statistically
data. Therefore, the study of the environment, significant, based on the three tested illumination
population, and device challenges is important to levels; low (7-12 lux), medium (407 – 412 lux),
the field of biometrics, and is an area of study and high (800 – 815) lux. This signifies that
currently being undertaken at Purdue University. when your lighting conditions are not constant
for verification, the enrollment light level should
2. Biometric Performance and the be as high as possible. For the low and medium
enrollments, the illumination used for the
Environment. verification attempts was statistically significant,
which meant that enrollments using low and
Numerous experts, reports and papers have medium illumination, defined for this evaluation,
stated that illumination greatly affects facial are not good to use when your environmental
recognition performance, but have not provided lighting conditions are not constant for
significant results that have showed this [4-12]. verification. This research also revealed that
Kukula and Elliott  evaluated the there was a statistically significant difference
performance of a commercially available 2D between enrollment illumination level and the
face recognition algorithm across three verification illumination level at = 0.01,
illumination levels. The illumination levels were insinuating that the enrollment illumination level
determined by collecting data for sixty minutes is a better indicator of performance than the
in 2 locations: a local campus restaurant (low illumination level of the verification attempts.
3. Fingerprint Image Quality and the Elderly
Discussion of poor image quality issues
regarding elderly fingerprints occurs in the
biometric literature [14-18]. Recently, an Ad
Hoc group was formed by INCITS M1 to
address image quality issues, and a working
document has been drafted which addresses two
types of image quality: effectiveness quality and
fidelity quality. Effectiveness image quality is a
score that determines the usability of an image
from the biometric system standpoint, whereas
fidelity image quality determines how closely
images of the same individual match, without
regard for system usability. In the research Figure 3. Visual display of the effectiveness
addressed in this paper, effectiveness image quality of the low quality dry image. A green
quality is a more robust measure, since poor area has good image quality, and all other colors
image quality issues pose problems for indicate poor image quality.
fingerprint recognition systems during the
enrollment, verification, and identification
processes. Therefore, the problem proposed by
 was to determine the impact that particular
variables, namely age and moisture, had on the
effectiveness quality of fingerprint images.
Non-uniform and irreproducible contact
between the fingerprint and the platen of a
fingerprint sensor can result in an image with
poor effectiveness quality (Figures 2, 3 & 4).
Non-uniform contact can result when the
presented fingerprint is too dry (Figure 3) or too
wet, and irreproducible contact occurs when the
fingerprint ridges are semi-permanently or
permanently changed due to manual labor,
injuries, disease, scars or other circumstances Figure 4. Visual display of the effectiveness
such as loose skin . quality of the high quality normal image. A
green area has good image quality, and all other
colors indicate poor image quality.
These two contact issues can result when an
elderly user (62 and older) presents their
fingerprint to the fingerprint device . As
individuals age, their skin becomes dryer, sags
from the loss of collagen, and becomes thinner
and loses fat due to the loss of elastin fibers,
which decreases the firmness of the skin ,
and is likely to have incurred semi-permanent or
permanent damage over the life of the individual.
Two areas of the fingerprint recognition system,
Figure 2. (a). Dry image (left) of low quality and data collection and signal processing, are
a (b).normal image (right) of high quality. Both affected by non-uniform contact, irreproducible
images were acquired from an optical sensor  contact, and the inability of interaction between
the system and the user.
The problem of interaction between the user
and the system affects the sub-category of
presentation within the data collection silo. If the
user cannot present their fingerprint to the device
due to arthritis or other age related factors, then
enrollment and subsequent verification or
identification attempts are not possible through
fingerprint recognition. Irreproducible and non-
uniform contacts affect the sub-categories of
feature extraction and quality control, within the
signal-processing silo. Non-uniform contact
tends to produce images of low quality, resulting
in poor feature extraction of the presented
fingerprint. Factors responsible for
irreproducible contact, such as a temporary
injury, introduce false minutiae points, creating
an inaccurate representation of the individual’s Figure 5. Example Pearson correlation of image
fingerprint, therefore reducing the effectiveness quality vs. age (ratio).
quality of the fingerprint image. Furthermore, the
effectiveness quality of a captured image is one The second hypothesis stated that there is no
of the most important aspects for a biometric statistically significant difference between the
system, as it is this quality parameter that fingerprint moisture content of the age groups
determines whether a captured image is 18-25 and 62+. This hypothesis was rejected at
acceptable for further use within the biometric α=0.01 for both index fingers when used with
system, or not. The effectiveness quality of a the optical sensor and it was rejected for the right
presented fingerprint image is developed and index finger in conjunction with the capacitance
processed by the quality control function of the sensor. However, this hypothesis failed to be
biometric system, and a score, based on the rejected at α=0.01 for the left index finger and
image’s usability, is assigned to that image. It is the capacitance device. The overall finding of
these quality scores and captured images that this study implies that more emphasis should be
provide the data used by the biometric system to placed on an individual’s age, rather than the
determine an accept/reject decision. moisture of the finger when developing a
For this study two population age groups were fingerprint recognition system.
used, the elderly (62+) and a younger (18-25
years) group, although no subject was excluded
from participating based on age. However, data
from subjects not falling into one of these age
4. Device Impacts on Dynamic
groups were excluded from the analysis. The Signature Verification.
minimum age was set to 18 years old since
individuals this age and older are considered The use of written signature as a symbol for
adults and do not need a guardian’s consent to business and personal transactions has a long
participate. The maximum age of the younger history. Before the collection of data, a decision
population was set to 25 years old, in order to on the acquisition of the signature signal is
establish the typical age range for college or critical to the capture of the signature: on-line or
university students. Two different devices were dynamic signature verification has various
used in the research, capacitance and optical methods of acquiring data. Each of the various
fingerprint sensors. The population size was 54 devices used in studies demonstrate different
in both the 18-25 and 62+ categories. physical and measurement characteristics.
The results show that the difference of According to there is no consensus within
effectiveness image quality data was statistically the research community on the best method of
significant at α=0.01 for each index finger, as capturing this information, although the table-
well as for each sensor. Therefore, the hypothesis based digitizer tablet is by far the most popular.
stating that there is no statistically significant As the hardware progresses towards providing
difference in the fingerprint quality between the solutions to mobile and electronic commerce,
age groups 18-25 and 62+ is rejected at α=0.01. there may be a shift away from these table-based
The basic findings are shown graphically in digitizers.
Figure 5, the Pearson correlation of image Hardware properties can affect the variables
quality vs. age (ratio). collected in the data acquisition process, and
therefore the quality and performance of the examiners can compare signatures captured on
device. Different digitizers and software enable the same type of device.
the capture of different parameters of the
signature, at differing resolutions and speed. 5. Conclusion
Typical acquisition features include 'equivalent-
time-interval-sampled, x, y co-ordinates of pen- Environment, image quality, and device
tip movements, pen pressure, pen altitude (the selection play an important role in the successful
height of the pen from the digitizer) and pen implementation of a biometric system. The
azimuth (the angle between the reference line research shown here indicates that there are still
and the line connecting a point with the origin) some challenges associated with biometrics, but
. that these challenges can be overcome through
The central focus of  was to examine algorithm improvements. The results of the face
whether there are statistically significant recognition evaluation showed there are still
differences in the measurable variables across significant challenges with regard to illumination
devices. The volunteer crew was made up of 203 levels and face recognition especially at lower
individuals whose demographics tended toward light levels, which correlates with other research
the 19-26 age groups due to the composition of that has been done [4-12]. Further research is
the testing environment. Although not planned that will examine the performance of a
representative of the U.S. population except for three dimensional face recognition algorithm
gender, it is representative of the population across three illumination levels, to examine how
found in college and university environments. 3D face algorithms respond to illumination
Males accounted for 66% of the volunteer group, changes. For image quality and fingerprint
and females 35%, who were typically older than recognition, further research is planned that
the males. Right handed members of the includes other sensors such as optical and
population accounted for 91%, and left-handed thermal technologies, as well as swipe-based
members accounted for 9%. capacitance sensors, small area capacitance
Based upon the data, the major conclusion that sensors, and different forms of silicon sensing
can be drawn from the study is that there are chips, such as radio frequency based sensors.
significant differences in the variables across Also, a future study will examine the image
devices, yet these variables are not significantly quality of fingerprints collected on devices in
different within device families (Wacom, Palm, different outdoor settings. The results of such a
and Interlink E-pad). When these devices are study would be important in understanding if
grouped together, these variable differences image quality is increased or decreased over
continue to be significant. Dynamic signature devices depending on the time of year or season.
verification is used within the realms of For signature verification, a further study into
electronic document management and contracts, forgeries will be undertaken shortly, that
and will typically be verified only if the establishes which variables in a dynamic
document validity is questioned. As a result, the signature verification algorithm are susceptible
type of device needs to be attached in some way to forgery.
to the signature data so that the document
 Elliott, S., A comparison of on-line
Dynamic Signature Trait Variables across
6. References different computing devices, in Industrial
Technology. 2001, Purdue University: West
 Wayman, J. and L. Alyea, Picking the Lafayette.
Best Biometric for Your Application, in National
Biometric Test Center Collected Works, J.  Alim, O., et al. Identity Verification
Wayman, Editor. 2000, National Biometric Test Using Audio-Visual Features. in National Radio
Center: San Jose. p. 269-275. Science Conference. 2000. Egypt.
 Sickler, N. and S. Elliott, Evaluation of  Mansfield, A. and J. Wayman, Best
Fingerprint Quality across an Elderly Practices in Testing and Reporting
Population vis-a-vis 18-25 Year Olds, in Performances of Biometric Devices. 2002,
Industrial Technology. 2003, Purdue University: National Physical Laboratory: Teddington,
West Lafayette. England.
Paper presented at the International Biometrics
 Phillips, P., P. Rauss, and S. Der, 2002, Amsterdam.
FERET (Face Recognition Technology)
Recognition Algorithm Development and Test  Buettner, D. J. (2001). A Large-Scale
Report. 1996, U.S. Army Research Laboratory. Biometric Identification System at the Point of
p. 73. Sale. Retrieved September 29, 2002, from the
World Wide Web:
 Phillips, P., et al., An Introduction to http://www.itl.nist.gov/div895/isis/bc2001/FINA
Evaluating Biometric Systems. IEEE Computer L_BCFEB02/FINAL_2_Final%20Doug%20Bue
Society, 2000(February 2000): p. 56 - 63. ttner%20Brief.pdf
 Podio, F., Personal Authentication
Through Biometric Technologies. IEEE, 2002.  Jain, A., Hong, L., & Pankanti, S.
4th International Workshop on Networked (2000, February). Biometrics: Promising
Appliances: p. 57 -66. frontiers for emerging identification market.
Comm. ACM, 91-98.
 Sanderson, S. and J. Erbetta.
Authentication for Secure Environments Based  Jain, A. K., & Pankanti, S. (2001).
On Iris Scanning Technology. in IEEE Advances in Fingerprint Technology (2nd ed.).
Colloquium on Visual Biometrics. 2000: IEEE. New York: Elsevier.
 Sims, D., Biometric Recognition: Our  Jiang, X., & Ser, W. (2002). Online
Hands, Eyes, and Faces Give Us Away. IEEE Fingerprint Template Improvement. IEEE Trans.
Computer Graphics and Applications, 1994: p. Pattern Analysis and Machine Intelligence,
14-15. 24(8), 1121-1126.
 Starkey, R. and I. Aleksander. Facial  Jain, A. K., Hong, L., Pankanti, S., &
Recognition for Police Purposes Using Bolle, R. (1997). An Identity-Authentication
Computer Graphics and Neural Networks. in System Using Fingerprints. Proc. IEEE, 85(9),
IEEE Coloquium on Electronic Images and 1,365 - 1,388.
Image Processing in Security and Forensic
Science. 1990: IEEE.  American Academy of Dermatology,
Mature Skin. 2002.
 Sutherland, K., D. Renshaw, and P.
Denyer. Automatic Face Recognition. in First  Leclerc, F. and R. Plamondon,
International Conference on Intelligent Systems Automatic Signature Verification: The State of
Engineering. 1992. Piscataway, NJ: IEEE. the Art - 1989 - 1993. International Journal of
Pattern Recognition and Artificial Intelligence,
 Kukula, E. and S. Elliott, The Effects of 1994. 8(3): p. 643-660.
Varying Illumination Levels on FRS Algorithm
Performance, in Industrial Technology. 2004,  Yamazaki, Y., Y. Mizutani, and N.
Purdue University: West Lafayette. p. 98. Komatsu. Extraction of Personal Features from
Stroke Shape, Writing Pressure, and Pen
 Behrens, G. (2002, March). Assessing Inclination in Ordinary Characters. in Fifth
the Stability Problems of Biometric Features. International Conference on Document Analysis
and Recognition. 1998.