Hazards of Biometric Authentication in Practice

IT in Industry, vol. 4, no. 1, 2016 Published online 30-Sep-2016
Copyright ISSN (Print): 2204-0595
© Phang and Pavlovski 2016 34 ISSN (Online): 2203-173
Hazards of Biometric Authentication in Practice
Samantha S. S. Phang
Commonwealth Bank of Australia
Digital Protection Group
Sydney, NSW, Australia
Christopher J. Pavlovski
Commonwealth Bank of Australia
Digital Protection Group
Sydney, NSW, Australia
Abstract—With the increase in cyber threats and attacks
many institutions are exploring how newer technologies may be
applied to strengthen the way users are verified when bestowing
permissions for carrying out web transactions. In particular,
many institutions are under increasing pressure to improve the
security instruments used to authenticate users, while permitting
access to their personal records to approve transactions. Whilst
multifactor authentication protocols have been adopted to
validate more sensitive transactions, this has added an additional
physical interaction during the verification process. More
recently, the industry has turned its attention to the use of
biometric authentication as a way to securely verify user
identities. This has reduced the complexity associated with
existing authentication processes that require passwords, tokens,
and challenge-response keywords. This paper explores these new
authentication techniques, discussing the benefits while
highlighting the challenges in practice to using biometrics. In
particular, identity theft of biometric markers and its potential
impact to customers and liability challenges for institutions are
presented.
Keywords—biometric authentication; cyber threat; identity theft
I. INTRODUCTION
As newer digital technologies evolve to become available
for use in industry applications the opportunity exists to
improve the way customer interact with on-line systems while
strengthening security and improving ease of use. Biometric
authentication is one such technology which has been
regarding as an advanced tool to improve the strength of the
verification process of users but also improve the usability
aspects by simplifying the authentication process for people.
Whilst a range of biometric technologies have been in use for
some time for several authentication systems, such as access to
restricted areas or sensitive (secret) facilities, it has recently
gained attention as a technology option for mainstream
industry. In particular, it has been suggested as a practical way
to strengthen and improve the authentication of on-line
customer wishing to conduct personal and sensitive
transactions.
Cyber threats have continued to increase in volume and
complexity and in some cases exponential growth has been
experienced in certain types of attack. The traditional means
for authenticating users has often relied heavily upon the
username and password credential. This has often been
strengthened with digital certificates, secure hardware tokens,
or the addition of multiple authentication challenges; as seen in
multi-factor authentication. There are several drawbacks to
these existing systems which have seen cyber attackers
successfully obtained credentials of the users. For instance, one
study estimated that 50% of users reuse their passwords [1]; it
follows that the attacker need only compromise one (less
secure) site to gain credentials for other potentially more
sensitive online accounts for the same user. Hence, there are
motivations for institutions to explore alternative and stronger
forms of authentication to counter these cyber threats. In
particular, biometric authentication has emerged as a strong
candidate to fulfil this need.
In this paper, the industry application of biometric
authentication is critically examined. The most recent
innovations to apply this form of authentication are assessed to
understand the benefits these new technologies bring and also
the potential challenges that may arise. Given that the security
protocols involve the use of the most sensitive human personal
data, biometric information, there is particular importance to
understand what risks may be encountered when applying such
tools in practice. Hence, the main contributions of this paper
include the following.
 The industry applications of biometric authentication
and the projected trends for use are assessed;
 Analysis is conducted of the industry challenges in
practice with the use of human biometric markers; and
 The potential consequences and liabilities to people and
institutions are assessed.
In the next section a review of the literature related to
biometric authentication is discussed. Section three discusses
the set of human biometric identifiers that have been
considered as tools in building or enhancing authentication
systems. In section four, several applications that have been
proposed for the industry in practice are explored. This is
followed by section five where the challenges and risks to
using biometric markers are evaluated with the risks and
liabilities for both the customer and business, in particular the
implications to the industry in the event of a data breach is
examined. The paper is concluded in section six with a
discussion of the key observations made in the paper while
several areas of further work are presented.
II. RELATED WORK
There has been a great deal of work related to the use of
biometrics for authentication purposes. Much of that work has
focused on applying a variety of biometric identifiers to
strengthen the existing authentication protocols and schemes.

While an adopted form of biometric authentication is its use
in border security, one study on the use of facial recognition
suggests that this is inadequate in large applications such
as border control [2]. Multimodal biometric schemes have
also been proposed for border control applications that
utilize facial recognition together with fingerprinting on e-
passports [3]. Further works suggests the use of multi-modal
biometric schemes can also be used to overcome some of the
limitations of using single biometric identifiers in
authentication [4].
Two factor authentication systems have also been proposed
as an approach for strengthening traditional username
password credential based systems. The use of a biometric
marker (voice) together with an additional (non-biometric)
authentication factor was analyzed in [5]; the authors conclude
that the second factor may not contribute to strengthen the
overall protocol. Combining biometrics with mobile
technology has also been studied together with a username
password authentication factor [6]. The paper examines the use
of facial, voice, and gestures, revealing issues in usability and
performance, in particular noting that facial and voice are not
universally usable. A further study reported an alternative view
in which biometrics are considered more usable in comparison
to passwords on a mobile device [7]. Several additional
approaches to using biometrics have also been investigated,
such as biometrics authentication as a service for enterprise
identity management [8], approaches to using biometrics in
authentication in ad-hoc networks without the presence of an
authentication server [9], and augmenting traditional web
applications with a voice biometric authentication capability to
improve confidence in the customer identity and reduce
transaction fraud [10].
Finally, we observe the literature related to understanding
the risk and challenges of using biometric [11–13]. In [11], the
authors disclose biometric uses and corresponding security and
privacy issues of using these, suggesting that biometrics does
indeed raise several privacy concerns and that a sound trade-off
between security and privacy may be necessary. A further
investigation of issues concerning biometric profiling is
presented in [12], where it is observed that biometrics may be
used as a source for profiling information with the risks
including loss of control over personal data, concerns in
discrimination, and legal implication. Schneier remarks that
while biometric identifiers are difficult to forge they are easy to
steal [13]. Moreover, he observes that biometric data are
unique identifiers but are not secrets; once it is stolen there is
no mechanism to revoke the identifier, it is effectively stolen
for life [13].
The work herein may be considered an extension of this
particular focus area of risk, as an in-depth assessment of
the risks and challenges are covered for institutions and
people. In particular, noting the potential harm that may be
caused (the human aspect), which in turn will ultimately
lead to financial and legal liabilities for institutions,
whether this is due to an institutional data breach or
the universal ability to covertly steal biometric marker from
people.
III. BIOMETRIC AUTHENTICATION MARKERS
Biometric identifiers for authentication purposes are
generally derived from two categories: i) physiology or ii)
behavioral human traits. Physiological traits as biometric
identifiers are related to the shape of body parts. This includes
fingerprints, hand geometry, palm print, facial appearance, iris
pattern, retina pattern and human DNA. Conversely, voice,
pulse rate, body heat signature, gait, keystroke dynamic, and
hand signature (pen pressure and signature speed) are biometric
identifiers related to pattern of a person’s behavior. We now
discuss in more detail some of the more commonly applied
traits for these two categories.
A. Physiological Biometric Identifiers
The fingerprint is uniquely identified by a pattern of ridges
and valleys, known as minutiae features, on the surface of the
fingertip. The fingerprint of each finger is different and is
unique for each individual, including identical twin. Fingerprint
formation is fully developed during the first seven months of
fetal development. The pattern remains stable over a person’s
lifetime with exception of damage caused by external factors
such as injury or disease. The distinct features of the fingerprint
are segmented and extracted through advanced image
processing techniques after the live scan. Correlation-based
matching and pattern-based (ridges or valleys) matching are the
common fingerprint identification techniques used.
Hand geometry is a biometric that identifies an individual
by the shape, size of palm, length and width of fingers of the
hand. Standard optical camera or flat-bed scanners are common
devices used to capture hand images in hand geometry
recognition systems. In many cases finger position guides are
used to ensure consistency of hand image capture. The key
features of the person hand are extracted from the black and
white silhouette of the digitized grey scale hand image. Some
of the common matching approaches used include Euclidean
distance metrics, correlation method and principal component
analysis [14].
The iris is composed of a random texture pattern within the
human eye and is unique for each individual including identical
twin. Iris patterns on the left and right eyes are also different.
The iris pattern stabilizes within the first 2 years of life and
remains unchanged unless there is damage due to eye disease
(e.g. cataract) or unsuccessfully eye surgery. A common
approach for iris recognition systems is to apply near infrared
light to acquire iris images. This is more effective in revealing
rich texture for dark brown eyes compared to light colored
eyes. The iris code can be generated in one dimension using
normalization resolution levels of iris features, or two
dimensions using techniques such as Gabor filters [15] and
Laplacian pyramid [16]. The Hamming distance [17] and
Fisher discriminant [18] are some of the well-known matching
approaches used to measure the similarity of two irises. A
related biometric is the retina scan which involves detects the
patterns of veins in the back of the eye to accomplish
recognition.
Palm-print recognition measures the inner surface of the
hand. The process obtains geometric features (i.e. palm shape),
minutiae features, principal lines, wrinkles and delta point

features that are unique to the individual. Identical twins have
enough distinctive palm-print features for recognition
purposes. Given the richness and breadth of palm-print
features, it is considered a more accurate biometric identifier
compared to hand geometry and fingerprints. Methods used to
represent palm-prints can be divided into five categories [19],
these are: i) line-based, ii) appearance-based, iii) local statistic
based, iv) global statistic based, and v) coding based. In
addition to the fingerprint and hand geometry matching
algorithms, the Hamming distance approach is also commonly
used to match two palm-prints.
Facial recognition involves identification based upon the
attributes of a person face. Recognition data is extracted in
either two or three dimensional facial images. There are two
broad categories to face recognition approaches. Feature-based,
which uses properties and geometric (e.g. areas, distances and
angles) relations of between facial features as recognition
descriptors. The second is an appearance-based method which
involves an analysis of the face image intensity pattern. Some
of the popular matching algorithms used include Principal
component analysis, Linear Discriminant Analysis and Tensor
faces, Manifold Learning method, and Kernel method [20].
Deoxyribonucleic acid (DNA) is classified as a chemical
biometric. This marker may be used for authentication and the
identification of an individual is achieved through the analysis
of partial segments of the DNA strand.
B. Behavioural Biometric Identifiers
Voice as an authenticator may be applied with a
combination of acoustic and behavioral patterns. The acoustic
patterns are influenced by the shape and size of vocal tracts,
mouth, and nasal cavities, while the behavioral patterns are
defined by voice pitch, speaking style, and sociolinguistic trait.
The acoustic patterns are more stable than behavioral patterns
over time due to age, medical conditions, and emotional state.
The key features extracted from a person voice forms the voice
print used for authentication. Template matching and feature
analysis are two widely used voice recognition approaches.
The goal of matching is used to find similarities between the
stored and the actual voice print. Template matching involves
detection of a near-exact match between a previously stored
voice print and the voice print to be authenticated. For feature
analysis, voice data for matching is processed using statically
models like Fourier transformations, hidden Markov models or
Gaussian mixture models to generate the voice print. Text-
dependent and text-independent are two types of commercially
used voice recognition systems. The matching of the former
system is based on utterance of fixed predetermined phrase for
enrollment and for verification, whilst there is no constraint on
the speech content for the latter in the matching process.
Hand written signatures are a behavioral characteristic of a
person signing their name. Signature recognition can be
operated in off-line or on-line manner. Off-line analysis detects
the similarity of the signature shape for two digitized static
signature images. On-line mode refers to acquiring signature in
real time using acquisition devices like touch screens or
digitizing tablets and capturing dynamic features like position
trajectories, timing, pressure, speed of signing and size of
signature; which are very difficult to mimic. Individuals must
sign their name multiple times during an enrolment process.
Enrolment can be divided into reference-based and model-
based approaches depending on the matching strategy. In
reference based systems a set of signature templates are
generated, with the features extracted from the set of enrolled
data. While a model-based system involves a statistical model
which describes the behavior of the signor which is estimated
from the enrolled data. Popular matching techniques applied
for signature recognition are dynamic time warping, hidden
Markov models and vector quantization [21].
Gait recognition is the identification of a person based on
the manner in which they walk. This can be used to from a
distance which make this trait suitable appropriate in
surveillance applications. Model based gait recognition
techniques involve extraction of spatial-temporal attributes of a
moving individual. This is derived from the silhouette or
optical flow associated with a set of dynamically moving
points of the moving human body and used to describe the gait
of an individual. Approaches that recognize individual through
binary gait silhouette sequence belong to appearance-based
approaches.
Keystroke dynamic is determined by how a person types on
a keyboard and is based upon habitual typing rhythmic
patterns. While this trait is not as unique as other biometric
traits, the minor variation is said to offer sufficient
discriminatory information to identify a person. Some of the
common keystroke recognition techniques include static at
login which observes typing pattern using a known keyword or
phrase, periodic dynamic that analyses the typing pattern
characteristic over a specific timeframe, and continuous
dynamic which monitors the typing behavior during a series of
interactions. Other techniques include keyword-specific,
achieved by continuously monitoring the typing pattern for
specific set of keywords and digraph latency which measures
the time between the key-up and next key-down action.
IV. BIOMETRIC APPLICATIONS IN PRACTICE
In the past, the application of biometric technology has
predominantly been used for forensic purposes such as
fingerprint collection at a crime scene or determining heritage
via DNA matching. The adoption of biometric technology to
solve other business problems has increased as the technology
has matured. These solutions can be generally categorized into
commercial and government applications. The government
applications may include national identification cards, driving
licenses, and passports. These have subsequently been
extended for use in border control, passport control and
welfare-disbursement. For example, recent border control
systems allow travelers to use a kiosk then pass through a facial
recognition system that is compared with the image stored on
an e-passport microchip to verify the person.
Some governments have adopted Iris recognition
technology for social benefit claim, while humanitarian
organizations use this for aid distribution control to manage aid
entitlement for people. More recently, Amber Alert, a face
recognition technology, has been launched by government
agencies in various countries and social media company to find

missing persons [22, 23]. In some countries biometric
technology is used to prevent voter fraud. The Mobile Offender
Recognition and Information System (MORIS) has been
developed for police officers to scan biometrics and retrieve
any criminal history of a subject in near real time [24].
Surveillance monitoring is another application where law
enforcement authorities apply facial identification technology
to identify criminal in the live surveillance streaming at airport.
This type of application is also common in places such as
Casinos to identify and alert relevant staff to the presence of
blacklisted or high risk customers.
The commercial applications of biometric technology are
more extensive. The applications include wireless
authentication, device security authentication, logical access
control, physical access control, negative recognition, time and
attendance, and transactional authentication. Laptops and
notebooks are now built with biometric scanning devices that
enable a user to quickly logon. Additionally, many smart
phones are equipped with cameras and biometric scanning
tools for authentication. These ideas have unlocked a range of
network, online and mobile applications to include biometric as
an alternative authentication method. For instance, there are a
number of Android applications that employ facial recognition
to ensure the application is only accessible by the purchaser or
selected user. There are also applications available that allow
the user to encrypt their document using their hand written
signature or to generate cryptographic keys based on time
functions of their hand written signatures.
Banking, telecommunications, and the health sector are the
few major industries that use biometrics for granting controlled
logical access. The solutions rely upon the native biometric
capability built into smartphones and notebooks and enable
customers to access their financial and phone accounts. Some
financial institutions use passive speaker recognition to verify
telephone customers [25], while telecommunications carriers
have adopted voice recognition to identify telephone
customers, with the aim to reduce the operational cost of the
call center. The media industry uses voice biometrics to
control access to media content for media authors, producers,
and final users. In the health industry, biometric systems are
used by medical staff and patients to access patient electronic
medical records. Furthermore, some hospitals leverage hand
vascular systems in their medical supply dispensation systems
to ensure that restricted and expensive drugs are not stolen.
Biometric authentication for physical access control has
been widely used by the sporting and entertainment industry.
For example, controlling access to the Atlanta Olympic Village
was accomplished with the fingerprints of athletes, staff and
volunteers. The approach has been also used to manage paid
physical access where subject’s biometrics are used as the
ticket or pass. One motivation for theme park venues was to
prevent visitors buying unused ticket or partially used tickets
from others. Biometrics is often used to gain access to highly
sensitive restricted premises, such as access company data
center or a hospital operating room. Physical access control has
also widely been used in the government sector to control
highly restricted and sensitive premises such as nuclear plants.
Many government agencies have deployed biometric systems
in many sensitive and public areas for close monitoring and
negative recognition (i.e. prevent a single person from using
multiple identities by establishing whether the person is who
that person implicitly or explicitly denies being). Banks also
use biometrics as negative recognition to prevent lawbreaker
from creating new accounts or lines of credit.
Biometrics based time and attendance terminals are
becoming increasingly popular in many industries to ensure
that employees cannot clock-in for one another, thereby
preventing employee time theft. This concept has also been
adopted in the education industry to track accurate student
attendance, and in distance learning to ensure students actually
attended the minimum number of hours for online lectures.
Many banks have deployed biometric based Automatic Teller
Machines to prevent fraudulent withdrawals using fake, lost or
stolen credit cards. In the U.S, some retail stores have deployed
biometric systems to help customer cash their pay-checks or
make a payment after a purchase.
V. ADOPTION CHALLENGES AND LIABILITIES
In this section some of the challenges of adopting biometric
authentication in practice are presented. Invariably many of
these challenges may be addressed with improvements in
sensory technology. However, biometric identity theft presents
a difficult challenge to industry and is likely to compound as
biometrics adoption becomes more widespread. Moreover, an
analysis is presented of the potential liabilities that institutions
may incur due to biometric data breaches and general biometric
identify theft from individual due to malicious surveillance.
A. General Challenges in Practice
While the matching accuracy of fingerprints to identify a
person is relatively high, fingerprint recognition still faces
challenges with the poor quality of acquired data due to several
issues. This includes large pixel displacement of fingerprints
(resulting from different finger location on the sensor during
acquisition), non-linear distortion of converting three
dimensional objects to two dimensional images, and
differences in pressure applied on the sensor and varying skin
conditions of the finger. The sensor technologies available
belong to optical, ultrasound or solid-state (capacitive, thermal,
electric field, piezoelectric) families [26]. Additional problems
occur with the formation of scar tissue and dirt upon the
fingertips. Fingerprint residues are left almost everywhere by
people making them extremely vulnerable to illegal capture.
In general, hand geometry is not very distinctive trait as one
in every 100 people have very similar hand features to another
person, hence this identifier is not suitable for identification of
an individual when drawn from large population size. Similar
to the fingerprints, humans leave residue of the hands
constantly and hence the ability to capture hand geometry is
straightforward for a threat actor. Hand geometry varies across
a persons’ age due to physiological changes of the person (e.g.
physical growth or weight gain). Template adaptation
techniques that adapt the hand geometry to the individual’s
physiology changes over time and has shown to improve
matching performance [14]. The advantage of this identifier is
that factors like weather or individual anomalies do not affect
the accuracy of recognition. However, obstructions such as

rings, dirt, and large bandages could affect the matching
performance. Conversely, palm-print recognition faces both
challenges of physical changes over time and external
obstructions that hinder the performance of the system.
The matching performance of commercially available facial
recognition systems are constrained due to factors such as
facial poses, camera view points, ageing, makeup, and
eyeglass. In particular, illumination and expressions conditions
have been the focus of face recognition research. Computer
vision approaches such as Active Appearance Models and
Elastic Bunch Graph Matching have been shown to improve
the recognition performance for facial images with different
poses and facial expression [27].
The accuracy and the speed of iris recognition is very high.
The iris system has very low False Acceptance Rate (FAR), but
rather has a high False Rejection Rate (FRR) compared to other
biometric traits [28]. The major challenge of iris recognition is
the hippus movement of pupil due to changes in lighting
condition. While this movement is used to measure the
liveliness of the iris, it distorts the iris pattern which result in
high FRR when performing matches against it. Although
techniques can be used to restore the iris pattern to desired
pupil size [29]. Other major problems include poor quality of
iris images acquired due to eyelid, eyelashes, and reflections
hindering the iris features extraction.
While DNA is a very distinctive trait the key challenge in
the adoption of DNA based biometric system has been due to
the debate regarding its potential for misuse and this being
generally intrusive; (i.e. human profiling, and health status, and
ethics).
The human voice pattern not a very distinctive identifier
and the accuracy of voice recognition systems in authentication
are affected by changes in behavioral patterns of the voice,
background noise and differences in the devices used between
enrolment and voice recognition stage.
The gait of a person can be modified by many factors
which changes the normal locomotive traits, in some cases
permanently. The factors include extrinsic such as footwear
and clothing, intrinsic such as age, and physical attributes such
as weight & height. In addition, pathological insults can also
influence a persons’ gait; this includes trauma, musculoskeletal
anomalies, and psychiatric disorders.
Hand related behavioral biometrics such as keystroke
dynamic and signatures are not common. Factors like
emotional state, type of keyboard used and its position with
respect to the person could vary the person’s typing pattern.
While the key disadvantages of hand signature recognition are
the large intra-class variation and the behavior is influenced by
physical and emotional conditions.
B. Privacy Implictions of Biometric Authentication
While many early adopters of biometric technology see the
benefits in improving cost-effectiveness, improved efficiency,
and better customer service, this technology may well have
implications on human rights and privacy issues for those who
take part. Biometric data is mostly collected along with the
personal identifiable information of an individual. However,
when an organization collects data for one purpose and decides
to apply this for another purpose, without the person’s consent,
they are likely to be ethical and liable ramifications. For
instance, a recent lawsuit on facial recognition software is a
classic example where users sued an organization for violating
their privacy by identifying and tagging them in photos without
their consent [30].
Another privacy challenge is the covert collection of an
individual’s biometric without a person’s knowledge, and the
subsequent masquerade and use without consent. The human
face may now be captured in a very straightforward manner,
without the person being aware. This is more simplistic with
the era of social media where facial images or video can be
downloaded from a persons’ social media site. Similarly,
fingerprint can be easily obtained from latent prints on any
touched surface.
Many biometrics, especially behavioral biometrics, could
reveal secondary information about an individual. This may
include general health disposition, the likely occupation, and
social economic status. In some cases, the secondary
information may be used to place those individuals at a
disadvantage. The majority of the biometric data captured and
stored are unregulated and there are very few regions that have
biometrics information privacy acts to protect the public from
misuse. Moreover, there is no law (to date) that restricts others
from collecting biometric data without a person’s knowledge.
While the regulatory constraints are not in place the prospect of
human biometric data being used beyond what is initially
consented to is very high.
When a personal identity is stolen today, one may
ultimately resort to changing their name. Given the intrinsic
properties of biometric identity to an individual, the ability to
change this identifier will no longer be available – once stolen
the person is impacted for the remainder of life and all
authentication systems that rely upon this data are effectively
compromised. Not only is it relatively easy to obtain raw
biometric data of a person in public, many biometric systems in
place have flaws in protecting both the biometric data and
personal identifiable information stored. For example, security
flaws are noted in an e-passport system [31], where attackers
can access the RFID in the passport, which contains digitally
signed biometric information, wirelessly without the passport’s
holder knowledge.
If the digitized biometric data is not encrypted either at rest
or in transit, it will be subjected to man-in-the-middle
(interception) attacks. Furthermore, if an institution trusts a
new biometric system beyond appropriate levels, then they run
the risk of assuming identities and transactions are legitimate
when they may not be. Moreover, they may initially place the
onus on the customer to show that a transaction is fraudulent,
rather than the institution demonstrating that the transaction
was legitimate. If an imposter can spoof a biometric
characteristic, perhaps by creating a false finger, they may be
able to enroll or use a service without having to produce the
traditional identity documents that would normally be required.

C. Biometric Identiy Theft: The Liabilities
The most obvious challenge with the use of biometric
markers for authentication is the propensity of these markers to
be easily stolen. Human biometric markers are generally
visible to everyone with people leaving physical residues on
everything we touch, everywhere we go. Hence, many traits
can be obtained in a generally straightforward manner using
commodity technology available in the marketplace. Moreover,
camera technology is sufficiently mature to enable high
resolution photography of facial features, geometric attributes,
and observable characteristics. For example, researchers from
Carnegie Mellon University have recently made covert
collection of iris scans, one of the most difficult biometric
markers to acquire, in good quality without the persons’
cooperation; this has been achieved from a distance of 12
meters from the target individual [32]. The most important
implication of this theft, as pointed out by Schneier [13], is that
once the biometric marker is stolen “it is stolen for life” and
can no longer be used again. Conversely, when a password is
stolen, this can be changed.
In general, there are two common biometric identity theft
scenarios: i) data breaches sustained by an organization and ii)
the general theft of biometric markers from illegitimate
surveillance by a threat actor. Moreover, where an institution
provides a biometric authentication capability to users without
an alternative authentication option, (i.e. the user has no choice
but to use it), it is likely that the institution is also liable for the
compromise of any customer account from biometric theft, due
to the ease of which biometric markers may be stolen.
Furthermore, once they are stolen, the institution will
ultimately require an alternative form of authentication, since it
is not possible to change biometric markers as one would easily
change a lost or stolen password.
The potential consequences to an organization include
detrimental impact to corporate brand, loss of reputational
status, and erosion of trust within the marketplace. Further,
there are likely to be regulatory penalties imposed by
authorizes on any data breach that pertains to biometric data.
There is also the likelihood of class action legal pursuit from
the aggrieved customers for compensation. Furthermore, the
potential for additional financial impact from competing or
partner organizations exists. A company which sustains a data
breach of customers’ biometric data may also be liable to pay
for the losses (and other maintenance costs) of other companies
which uses the same biometric data of compromised
customers.
VI. DISCUSSION AND CONCLUSIONS
There are several motivations to apply biometrics markers
for authentication purposes to the industry. This includes ease
of use for customers, cost-effective solution alternative, and
relative fast and efficient means of user authentication. The
application of these emerging techniques can also be viewed by
the industry as being market leading for the institution adopting
such technologies. While these benefits may be appealing there
are several key challenges facing the use of biometrics markers
that have been discussed. Many of the challenges are technical
obstacles in ensuring the authentication technologies function
as intended. However, the key challenge and implications of
biometric identity theft require much deeper consideration by
institutions considering the adopting of biometric markers for
authentication.
With conventional (non-biometric) identifiers, when a
person is victim to identity theft today, they may ultimately
resort to changing their name or identity; as this typically
involves non-biometric identifiers such as name, age, and
national identifier. This last resort measure is longer applicable
to biometric identifiers, since once stolen the person is
impacted from that point on and all IT systems that rely upon
the stolen biometric marker are also immediately
compromised.
Finally, the financial and legal impact that may be felt by
an organization that sustains a data breach of biometric data
may be considerable and long lasting. The impact may be felt
from its customers, the market, regulatory bodies, and
competing organizations. While the use of biometric markers is
still in its infancy, some of these risks may appear more
measured. However, as the technology becomes more widely
used and is becomes prevalent, the impacts are likely to grow
and become more substantial.
Notwithstanding, where institutions decide to provide a
biometric authentication mechanism it seems prudent that at a
minimum the user be able to opt-out of using biometric data
and be provided with a suitable alternative authentication
system that does not involve biometric markers.
ACKNOWLEDGMENT
The authors are employees of the Commonwealth Bank of
Australia. Any views or opinions expressed in this article are
the author's own and do not necessarily reflect the view of the
Commonwealth Bank.
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