This study compared fingerprint image quality and matching performance between healthcare workers and general populations. The healthcare population had significantly less skin oiliness than general populations, likely due to frequent hand washing. However, there were no significant differences found in fingerprint image quality scores between the populations. When compared to fingerprints collected using a capacitance sensor, the healthcare workers had a slightly higher false reject rate during fingerprint matching. Overall, the study found that differences in skin characteristics between healthcare and general populations had a minimal effect on fingerprint image quality and matching performance.

1. A Comparison of Fingerprint Image Quality and Matching
Performance between Healthcare and General Populations
Christine R. Blomeke, Stephen J. Elliott, Ph.D., Benny Senjaya, and Gregory T. Hales
Abstract— Research has shown for some age groups, quality of in the deployment of biometric systems in many different
fingerprints can impact the performance of biometric systems. A environments and applications, and one area that has gained
desirable feature of biometrics is that they are suitable for use attention in the United States is the healthcare environment,
across the population. This applied study examines the
especially post-HIPAA – the Health Insurance Portability and
performance of a fingerprint recognition system in a healthcare
environment. Anecdotal evidence suggested front line healthcare Accounting Act. In many medical practices and hospitals, the
workers may have lower image quality due to continued hand increased use of tablet PCs by physicians to access electronic
washing which may remove oils from their skin. During training, medical records lends itself to being secured through the use
individuals are told to add oil to their fingers by wiping oil from of biometrics. This would help to restrict access to electronic
their foreheads to improve the resulting quality of the medical records to those with the proper credentials.
fingerprints. In the healthcare population the authors tested,
Likewise, the availability of tablet PCs with biometric sensors
compared to two general populations (collected on optical and
capacitance sensors) there was a significant difference in skin built into them provides a solution that would not require
oiliness, but not in image quality. There was a difference across additional hardware. One of the motivators to this short
healthcare and non-healthcare groups in the performance of the research project was to investigate the claims of those that
fingerprint algorithm when compared against the capacitance have worked in the healthcare community near West
dataset. Lafayette, IN., that some deployed or trialed fingerprint
biometrics seem to have some issues with users, specifically
Index Terms—fingerprint recognition, image quality, front line nursing staff within that environment. The
performance, healthcare. motivational question to ask is whether the biometric modality
is not suitable to the healthcare environment, or whether the
I. INTRODUCTION proposed deployment was deficient in other areas. The
Biometrics, the automatic identification of individuals based authors initially thought that due to the nature of the clinical
on their physical or behavioral characteristics have been environment, especially glove wearing, and the need to
proposed and implemented for a number of different continually wash hands might lead to dryer skin which in turn
applications. These include single-sign on applications in might have an effect on the performance of the fingerprint
education, financial, and industrial applications. There are biometric system. Combined with any human-biometric
several desirable biometric attributes, both from a user, sensor interaction difficulties or lack of habituation from the
system, and biometric modality perspective. One such users, this could lead to a domino effect. This study examined
attribute is that the biometric system works with the proposed 30 individuals from a healthcare environment and compared
population. Over the past eight years there has been a growth them two datasets of 30 individuals, one dataset collected on
optical sensor, the other on a capacitance sensor.
Manuscript received June 6, 2009. This work was supported by the Dean
of Graduate Studies, College of Technology at Purdue University, West II. LITERATURE REVIEW
Lafayette, Indiana. Logical access control systems in the healthcare industry
C.R. Blomeke is a graduate research assistant in the Biometrics
Standards, Performance and Assurance Laboratory, Department of Industrial
have been primarily managed through the use of password
Technology at Purdue University, West Lafayette, IN 47907 USA (e-mail: systems, but there is potential for the use of biometric
blomekec@purdue.edu). technologies to help streamline access management systems.
S.J. Elliott is Director of the Biometrics Standards, Performance and It is reported that 30% of all information technology (IT)
Assurance Laboratory, and an Associate Professor in the Department of
related help desk calls at healthcare organizations are
Industrial Technology at Purdue University, West Lafayette, IN 47907 USA
(e-mail: elliott@purdue.edu). password-related problems with a yearly maintenance fee of
B. Senjaya is a graduate research assistant in the Biometrics Standards, $300 per user. According to [1] biometric technology would
Performance and Assurance Laboratory, Department of Industrial help healthcare providers comply with the Health Insurance
Technology at Purdue University, West Lafayette, IN 47907 USA (e-mail: Portability and Accountability Act (HIPAA). Within the
bsenjaya@purdue.edu).
G.T. Hales is a graduate research assistant in the Biometrics Standards, context of the HIPAA, biometrics falls within the
Performance and Assurance Laboratory, Department of Industrial administrative simplification (AS) provisions by providing
Technology at Purdue University, West Lafayette, IN 47907 USA (e-mail: unique identification. Whilst there are other types of
ghales@purdue.edu). authentication technologies available such as passwords,
personal identification numbers (PIN), card technologies and
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2. telephone call back, biometrics possesses several advantages quality and matching performance.
to many of these other systems or by layering them, providing
dual-factor authentication. Although the case for biometric III. METHODOLOGY
technologies has been made in the traditional environment of The subjects of the healthcare population were recruited on
PC access control, the question to ask is how biometric a voluntary basis from a local hospital. The data from
technologies perform in the healthcare environment, healthcare workers were collected from nurses involved in
specifically with front-line nursing staff. There have been a direct patient care (N = 30), and compared to individuals in
number of studies that analyzed the image quality scores the general population (N = 30). The individuals completed
between different age groups. In [2], the researchers the demographics survey and their skin characteristics of
concluded that there was a correlation between image quality moisture, oiliness, and elasticity were collected using the
and age regardless of the device or index finger. Another study Triplesense device from Moritex USA and subsequently
by [3] specifically studied the impact of age on fingerprint recorded. The Triplesense device uses electrical capacitance
recognition performance. The researchers used groupings for to measure the moisture level of the skin, an optical sensor to
age of 18-25, 26-39, 40-62, and 62+ years old and concluded measure the sebum (oiliness) level, and a supersonic vibration
that the means of the quality scores are different. sensor to measure the skin’s elasticity. The skin
Representative ages from the healthcare population would characteristics were collected from the index finger of the
cover the age groups of 26 and above. dominant hand. The skin characteristics were measured at the
One study [2] analyzed fingerprint moisture, a factor that beginning of the study but were not measured again prior to
could be influenced by age, and image quality for both interaction with the second capture device – the sensors were
capacitance and optical sensors. The data indicated a used in sequence and not separated by time. The visit time
statistically significant difference between the moisture with each participant lasted approximately 10 minutes, and it
content between the young (18-25 years old) and the elderly was assumed that the skin characteristics would not change
(62+ years old) for the right index finger using a commercially during this short interaction time.
available capacitance sensor. According to [4], capacitance
sensors react to different hydration (moisture) levels rather A. Optical Fingerprint Collection
than to light changes as an optical sensor does. The moisture Individuals from the healthcare population presented their
content of a person’s skin changes over time and it can be index fingers of the right hand followed by the left hand.
affected by a number of factors. In the healthcare Three images of each finger were collected using the optical
environment, repetitious use of sanitizing products or fingerprint sensor and saved for subsequent processing.
procedures could reduce the amount of moisture in the hands Individuals were allowed 10 attempts for the system to
of healthcare professionals. Another study [5] indicated that in successfully capture three images. The Aware image quality
order to prevent “healthcare-associated infections”, software package was used to analyze image quality scores,
compliance with specific hand hygiene practices is required in and Neurotechnology Verifinger, Version 6.0 was used to
a healthcare environment. According to [6] frequent exposure collect the images and analyze the system performance.
of skin to soap and water has significant effects on the
structure and function of the stratum corneum barrier. The B. Swipe Capacitance Fingerprint Collection
study denoted the majority of healthcare workers that Six images were collected from the right and left index
complied with hand hygiene procedures during their shift fingers of healthcare population participants and saved for
resulted in significant stratum corneum barrier damage that subsequent processing. Image quality scores were analyzed
did not recover within 14 days as a normal person would. The using Aware image quality software package and
effect of hand hygiene products with the base of emulsion Neurotechnology Verifinger, Version 6.0 was used to analyze
cleanser, liquid soap, and alcohol handrinse were investigated the system performance.
by [7, 8]. The emulsion cleanser reduced skin dryness while
C. General Population datasets
liquid soap increased skin dryness and redness and the alcohol
handrinse resulted in decontamination of the skin surface. Two general datasets comprised the comparison general
The literature contains very few evaluations of biometric population. The first dataset was created by selecting
systems deployed into healthcare settings. In 2001, the individuals of a similar age to the healthcare population. An
deployment of a fingerprint system was initialized to enroll attempt was made to minimize the age differences between the
physicians and nurses for access to the electronic medical groups as much as possible due to the natural changes in skin
records [9]. The white paper indicated that over 2,000 users corresponding to age. The elimination of age as a factor helps
were enrolled into the system and would progress to over to determine the potential differences in image quality scores
5,000 users at the end of phase two and would be extended to and matching performance to be related to skin characteristics
satellite facilities, yet there has not been a formal evaluation and the work environment rather than being compounded by
that has been published of the performance of the system. age. The first general population dataset was collected on an
While the literature offers some insight into potential optical sensor. The second population dataset was collected
differences in skin characteristics in healthcare and general on a capacitance sensor. We call these two general
populations, this study will seek to determine if these potential populations GPO and GPC. Both GPO and GPC consist of
N=30. The average age of the healthcare population was 42.7
differences exist and what impact it has on fingerprint image
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3. years, and the average age of the general population was 28.0 relationship between the skin characteristic and the resulting
years for the optical sensor and 36.6 years for the capacitance quality score. The scatter plots for each of the three skin
sensor. The datasets for the optical and capacitance sensor characteristics by image quality score are shown in Fig. 1 and
contain images from some but not all of the same individuals. Fig. 2. Inspection of the scatter plots do not reveal any clearly
Therefore, no direct comparisons should be made across defined relationships between oiliness, moisture, or elasticity
sensor types. and the image quality scores for neither the optical nor the
capacitance sensor regardless of the population. The quality
scores are evenly distributed across the range (0 – 100) of
IV. RESULTS each skin characteristic.
The results section is subdivided into sections representing
the description of the image quality and skin characteristics
and the analysis of the matching performance results. All
comparisons were made between populations using the same
sensing technology.
A. Image Quality and Skin Characteristics Analysis
The skin characteristics and output of the image quality
software were measured on a 100 point scale, with 100 being
high, and zero the lowest possible value. Table I. presents the
median values of quality, oiliness and elasticity, and the mean
value of the moisture content for each population and sensor
based on the non-normal or normal distribution of the values
respectively. The average image quality score over each
individual’s fingers was used to calculate the overall average
quality score for each population. There was no significant
differences in image quality between left and right index Fig. 1. Optical sensor image quality versus oiliness,
fingers for any given population and sensor, so the data was moisture, and elasticity (clockwise from top left) by
aggregated. population (1 = Healthcare, 2 = General Optical - GPO).
A Mann-Whitney U test was performed on the quality,
oiliness, and elasticity data as a non-parametric method for
comparing the medians of two independent populations
(α=0.05). A two-sample t-test was performed on the moisture
data. Statistical significant differences were found at the
significance level α=0.05 between the healthcare and general
populations for the variables oiliness and elasticity. The very
low values of oiliness for the healthcare population can be
attributed to the frequency of washing the hands with alcohol
based cleansers or antibacterial soaps which strip the skin of
its natural oils. The difference in skin elasticity measurements
across populations could be a function of the device
placement on the fingers or operator technique of using the
device.
Table I. Median quality, oiliness, and elasticity scores and
mean moisture score are given by sensor and population. Fig. 2. Capacitance sensor image quality versus oiliness,
moisture, and elasticity (clockwise from top left) by
Quality Oiliness Elasticity Moisture population (1 = Healthcare, 2 = General Capacitance - GPC).
Optical
Healthcare 63.64 1.00 92.00 28.40
GPO 66.21 19.00 69.50 28.90 B. Matching Performance Analysis
p-value 0.437 <0.001 0.001 0.910
The detection-error tradeoff curve is used to present the
Capacitance
performance of the dataset at various levels of a false accept
Healthcare 73.54 1.00 92.00 28.40
GPC 73.58 13.50 71.50 34.33 rate (FAR) and its corresponding false reject rate (FRR). Fig.
p-value 0.589 <0.001 0.002 0.187 3 shows the performance of the optical sensor datasets, with
the healthcare population performance curve overlaid on the
general population optical (GPO) curve. The overlapping
The skin characteristics were plotted against the quality
curves for all levels of FAR indicate that the two populations
scores for each population to determine if there is a visible
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4. perform similarly. This is in contrast to the results shown in and general populations, but does not adversely affect
Fig. 4, where the performance of each dataset is shown for the image quality scores and matching performance.
capacitance sensor. While at high levels of FAR, the two From this small study, it is concluded that the differences
populations have similar performance, the level of FRR is in skin characteristics between populations have minimal
approximately 0.06% for the healthcare population for all effect on the fingerprint image quality score and matching
levels of FAR less than 1%. performance. This is an interesting observation as it is in
contrast to instructions given to participants in other
deployments to add oil to the fingertips by rubbing their
forehead to obtain a better quality image. Secondly, these
observations conclude that the characteristics of the
healthcare population are from the same distribution as
those from the general population. This makes it possible
to extrapolate data from the general population to the
healthcare population for general skin characteristics and
performance analysis. There is still a need to continue
investigating the use of biometrics in the healthcare
environment in terms of determining the most suitable
modalities for the specialized departments and the
incorporation of deployments with the requirements of
HIPAA. The movement of patient identification towards
Fig. 3. DET curve of matching performance of the the use of biometric identifiers prompts a variety of
healthcare and general population (GPO) datasets from images research to be conducted to determine the appropriate
collected with the optical fingerprint sensor. biometric technologies to be deployed in an environment
where there is such variability in the traits that are
collectable based on the severity of illness and or injury.
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in the skin’s elasticity was detected between the healthcare
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