Biometric Authentication Technology - Report

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Chapter - 1
INTRODUCTION
Humans recognize each other according to their various characteristics for ages. We
recognize others by their face when we meet them and by their voice as we speak to them.
Identity verification (authentication) in computer systems has been traditionally based on
something that one has (key, magnetic or chip card) or one knows (PIN, password). Things
like keys or cards, however, tend to get stolen or lost and passwords are often forgotten or
disclosed.
To achieve more reliable verification or identification we should use something that
really characterizes the given person. Biometrics offer automated methods of identit y
verification or identification on the principle of measurable physiological or behavioura l
characteristics such as a fingerprint or a voice sample. The characteristics are measurable
and unique. These characteristics should not be duplicable, but it is unfortunately often
possible to create a copy that is accepted by the biometric system as a true sample. This is
a typical situation where the level of security provided is given as the amount of money the
impostor needs to gain an unauthorized access.
In this computer-driven era, identity theft and the loss or disclosure of data and
related intellectual property are growing problems. We each have multiple accounts and
use multiple passwords on an ever-increasing number of computers and Web sites.
Maintaining and managing access while protecting both the user's identity and the
computer's data and systems has become increasingly difficult. Central to all security is the
concept of authentication - verifying that the user is who he claims to be.
We can authenticate an identity in three ways: by something the user knows (such
as a password or personal identification number), something the user has (a security token
or smart card) or something the user is (a physical characteristic, such as a fingerprint,
called a biometric).
Passwords are cheap, but most implementations offer little real security. Managing
multiple passwords for different systems is a nightmare, requiring users to maintain lists of
passwords and systems that are inevitably written down because they can't remember them.
The short answer, talked about for decades but rarely achieved in practice, is the idea of
single sign-on. Using security tokens or smart cards requires more expense, more
infrastructure support and specialized hardware. Still, these used to be a lot cheaper than

biometric devices and, when used with a PIN or password, offer acceptable levels of
security, if not always convenience.
Biometric authentication has been widely regarded as the most fool proof - or at
least the hardest to forge or spoof. Since the early 1980s, systems of identification and
authentication based on physical characteristics have been available to enterprise IT. These
biometric systems were slow, intrusive and expensive, but because they were mainly used
for guarding mainframe access or restricting physical entry to relatively few users, they
proved workable in some high-security situations. Twenty years later, computers are much
faster and cheaper than ever. This, plus new, inexpensive hardware, has renewed interest
in biometrics.
Who are you? Do you belong here? What rights do you have? And how do I know
you're who you say you are? Those are the essential questions that any effective security
system must answer before a user can access a computer system, network or other protected
resource. We think this is what a password system does, but passwords are only one part
of an effective security system. That security system requires three separate elements -
identification, authentication and authorization - that together make up what's called access
control. When you log into a computer or network, the first thing you're asked for is a user
name or account name. But a user name offers little protection to the system. Therefore,
the system also usually prompts you for a password, a form of authentication.
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1.1 Authentication
The question, "How do I know you're who you say you are?," is in many ways, the
most important one. Unless it's answered satisfactorily, identification is incomplete and no
authorization can or should take place. But how does a system verify that a user is who he
says he is? Simply entering your password doesn't prove it's you. Someone else could know
your password. The answer lies in a strong authentication process. Basically, the following
three factors can be used to authenticate an individual:
1. Something the user knows. This is a reusable password, passphrase, personal
identification number or a fact likely to be known only to the user, such as his mother's
maiden name.
2. Something the user has. This could be a key, a magnetic-stripe card, a smart card
or a specialized authentication device (called a token) that generates a one-time
password or a specific response to a challenge presented by the server.

3. Something the user is. This depends on some inherent physical trait or
characteristic. Often called biometrics, examples of this form of authentica t ion
include: fingerprints, retinal (eye) patterns, hand geometry, voice recognition, facial
recognition, typing pattern recognition and signature dynamics (speed and pressure,
not just the outline).
These authentication factors are listed here from weakest to strongest as determined
by how difficult they are to forge or fake. By themselves, each of these methods offers
some security. However, each has its own problems or weaknesses.
Anyone can enter a password and, historically, reusable passwords have been
vulnerable to guessing, brute force and dictionary-based attacks. The second means of
authentication - something the user has - requires the user to possess an often difficult-to-replicate
device. However this stronger protection also costs more (typically tens of dollars
per device), and it requires contingency procedures in case a device is left at home, lost or
stolen. The third type of authentication - something the user is - is the most difficult to
defeat, but it has other problems. Biometric identification methods are subject to two types
of errors: false positives and false negatives. The first erroneously authenticates an
individual who shouldn't be authenticated; the second denies an individ ual who should be
authenticated. Error are not desirable, and it's important to know and verify error rates when
considering such a system. [1]
Another problem is that permanent physical changes or temporary ailments or
accidents can alter or render unreadable the measured characteristic. If you cut part of your
fingertip, you've changed what the fingerprint reader sees. Put on a Band-Aid, and the
reader can't see the fingerprint at all.
Finally, if the method is compromised, there's no way to give an individual a new
identifying characteristic. You can issue a new password or security token, but you can't
change his fingerprints or eye pattern.
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1.2 Two-Factor Authentication
For greatly increased security, the approach preferred by experts is to use two of the
three methods in combination - a process called two-factor authentication. For example, to
use a security token that generates a one-time password, you may need to enter a personal

identification number into the token itself. Similarly, a card-key can be used in combinat ion
with a biometric system.
This is essentially what happens when you check in at an airport ticket counter. You
hand over your ticket, which identifies you. Then you show a photo ID of some kind. This
is something you have with you, and it's biometric (something you are) in that the clerk has
to determine that the photo on the card matches you.
Once a user has been identified and authenticated, what remains is to grant him access
to whatever specific system resources have been approved. This authorization is usually
accomplished by looking up that user's entry in an access control list that delineates specific
rights and permissions. These can be based, among other things, on an individual's identit y
or job function, membership in a workgroup or other classification or time of day or day of
week.
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Authentication via Security Token
Security Token like Secure ID, a hardware authentication device, or security token,
provides greatly increased protection against spoofing or brute-force attacks. The time-synchronized
SecurID card from RSA Security Inc. in Bedford, Mass., has an LCD screen
that shows a string of numbers that changes every minute. The user types in his user name
at log in, then the number shown on the card. The host system knows what that number is
supposed to be for that user at that particular time. Some tokens don't show a number
continuously but require the user to enter a PIN on the card itself before the number is
displayed, thus providing two-factor authentication.

Challenge-Response Systems with a token-based Challenge-Response system, the
system displays a number (the challenge) when you log in. The user types this number into
his token, which encrypts that to produce a second number (the response). The user enters
the response into the computer. The host performs the same operation on the challenge,
then compares its result to the user's response. If they match, the user is authenticated.
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1.3 What is Biometric?
The term biometrics is derived from the Greek words Bio & Metric. The term
Biometrics relates to the measurement (metric) of characteristics of a living (Bio) thing in
order to identify a person. Biometrics uses various physiological or behavioura l
characteristics. Common physiological biometric measurements include fingerprints, hand
geometry, retina, iris, facial images etc. While common behavioural biometric
measurements include signatures, voice recordings, keystroke rhythms etc. With an
increasing importance of security, there is a need to guaranty that only authenticated users
have access to the system. In recent years, biometrics authentication has seen considerable
improvements in reliability and accuracy, with some of the traits offering good
performance. However, even the best biometric traits till date are facing numerous
problems some of them are inherent to the technology itself. Biometric authentica t ion
systems generally suffer from enrolment problems due to non-universal biometric traits,
insufficient accuracy caused by noisy data acquisition in certain environments. Biometr ic
measurements are inherently varied because of the existence of back-ground noise, signal
distortion, biometric feature changes and environmental variations. Identification based on
a single bio-metric trait may not be sufficiently robust and it has a limited ability to
overcome spoofing.
One way to overcome these problems is the use of multi-biometrics. A mult i
biometric system uses multiple sensors for data acquisition. This allows capturing multiple
samples of a single biometric trait and/or samples of multiple biometric traits. This

approach is enables to provide significant improvement over unimodal biometric system in
terms of higher accuracy.
Biometric System Components and Process
Four major components are usually present in a biometric system:
A mechanism to scan and capture a digital representation of a living person’s
biometric characteristic.
Software to process the raw data into a format (called a template) that can be used
for storing and matching.
Matching software to compare a previously stored biometric template with a
template from a live sample.
An interface with the application system to communicate the match result.
Two different stages are involved in the biometric system process – enrollment and
matching.
Enrollment. As shown in Figure 1, the biometric sample of the individual is captured
during the enrollment process (e.g., using a sensor for fingerprint, microphone for speaker
recognition, camera for face recognition, camera for iris recognition). The unique features
are then extracted from the biometric sample (e.g., image) to create the user’s biometric
template. This biometric template is stored in a database or on a machine-readable ID card
for later use during a matching process.
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Figure 1. Example Enrollment Process

Matching. Figure 2 illustrates the biometric matching process. The biometric sample is
again captured. The unique features are extracted from the biometric sample to create the
user’s “live” biometric template. This new template is then compared with the template(s)
previously stored and a numeric matching (similarity) score(s) is generated based on a
determination of the common elements between the two templates. System designers
determine the threshold value for this verification score based upon the security and
convenience requirements of the system.
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Figure 2. Example Matching Process
Biometrically enabled security systems use biometrics for two basic purposes:
identification and verification.
Identification (one-to-many or 1: N comparison) determines if the individual exists within
an enrolled population by comparing the live sample template to all stored templates in the
system. Identification can confirm that the individual is not enrolled with another identit y
or is not on a predetermined list of prohibited persons. The biometric for the individua l
being considered for enrollment should be compared against all stored biometrics. For some
credentialing applications, a biometric identification process is used at the time of
enrollment to confirm that the individual is not already enrolled.
Verification (one-to-one or 1:1 comparison) determines whether the live biometric
template matches with a specific enrolled template record. This requires that there be a
“claim” of identity by the person seeking verification so that the specific enrolled template
record can be accessed. An example would be presentation of a smart card credential and
matching the live sample biometric template with the enrolled template stored in the smart

card memory. Another example would be entry of a user name or ID number which would
point to an enrolled template record in a database.
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1.4 A System Model
Although these devices rely on widely different technologies, much can be said
about them in general. Figure 1.1 shows a generic biometric authentication system divided
into five subsystems: data collection, transmission, signal processing, decision and data
storage. We will consider these subsystems one at a time.
1.4.1 Data Collection
Biometric systems begin with the measurement of a behavioural/physiologic a l
characteristic. Key to all systems is the underlying assumption that the measured biometric
characteristic is both distinctive between individuals and repeatable over time for the same
individual. The problems in measuring and controlling these variations begin in the data
collection subsystem.

The user’s characteristic must be presented to a sensor. The presentation of any
biometric characteristic to the sensor introduces a behavioural (and, consequently,
psychological) component to every biometric method. This behavioural component may
vary widely between users, between applications, and between the test laboratory and the
operational environment. The output of the sensor, which is the input data upon which the
system is built, is the convolution of: (1) the biometric measure; (2) the way the measure is
presented; and (3) the technical characteristics of the sensor. Both the repeatability and the
distinctiveness of the measurement are negatively impacted by changes in any of these
factors. If a system is to be open, the presentation and sensor characteristics must be
standardized to ensure that biometric characteristics collected with one system will match
those collected on the same individual by another system. If a system is to be used in an
overt, non-cooperative application, the user must not be able to wilfully change the
biometric or its presentation sufficiently to avoid being matched to previous records.
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1.4.2 Transmission
Some, but not all, biometric systems collect data at one location but store and/or
process it at another. Such systems require data transmission. If a great amount of data is
involved, compression may be required before transmission or storage to conserve
bandwidth and storage space. Figure shows compression and transmission occurring before
the signal processing and image storage. In such cases, the transmitted or stored compressed
data must be expanded before further use. The process of compression and expansion
generally causes quality loss in the restored signal, with loss increasing with increasing
compression ratio. The compression technique used will depend upon the biometric signal.
An interesting area of research is in finding, for a given biometric technique, compression
methods with minimum impact on the signal-processing subsystem.
If a system is to be open, compression and transmission protocols must be
standardized so that every user of the data can reconstruct the original signal. Standards
currently exist for the compression of fingerprints (Wavelet Scalar Quantization), facial
images (JPEG), and voice data (Code Excited Linear Prediction). [5]
1.4.3 Signal Processing
Having acquired and possibly transmitted a biometric characteristic, we must
prepare it for matching with other like measures. Figure divides the signal-proces s ing

subsystem into four tasks: segmentation, feature extraction, quality control, and pattern
matching.
Segmentation is the process of finding the biometric pattern within the transmit ted
signal. For example, a facial recognition system must first find the boundaries of the face
or faces in the transmitted image. A speaker verification system must find the speech
activity within a signal that may contain periods of non-speech sounds. Once the raw
biometric pattern of interest has been found and extracted from larger signal, the pattern is
sent to the feature extraction process.
Feature extraction is fascinating. The raw biometric pattern, even after
segmentation from the larger signal, contains non-repeatable distortions caused by the
presentation, sensor and transmission processes of the system. These non-controllable
distortions and any non-distinctive or redundant elements must be removed from the
biometric pattern, while at the same time preserving those qualities that are both distinct ive
and repeatable. These qualities expressed in mathematical form are called “features”. In a
text-independent speaker recognition system, for instance, we may want to find the
features, such as the mathematical frequency relationships in the vowels that depend only
upon the speaker and not upon the words being spoken, the health status of the speaker, or
the speed, volume and pitch of the speech. There are as many wonderfully creative
mathematical approaches to feature extraction as there are scientists and engineers in the
biometrics industry. You can understand why such algorithms are always considered
proprietary. Consequently, in an open system, the “open” stops here. In general, feature
extraction is a form of non-reversible compression, meaning that the original biometric
image cannot be reconstructed from the extracted features. In some systems, transmis s ion
occurs after feature extraction to reduce the requirement for bandwidth.
After feature extraction, or maybe even before, we will want to check to see if the
signal received from the data collection subsystem is of good quality. If the features “don’t
make sense” or are insufficient in some way, we can conclude quickly that the received
signal was defective and request a new sample from the data collection subsystem while
the user is still at the sensor. The development of this “quality control” process has greatly
improved the performance of biometric systems in the last few short years. On the other
hand, some people seem never to be able to present an acceptable signal to the system. If a
negative decision by the quality control module cannot be overridden, a “failure to enroll”
error results.
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The feature “sample”, now of very small size compared to the original signal, will
be sent to the pattern matching process for comparison with one or more previously
identified and stored feature templates or models. We use the term “template” to indicate
stored features. The features in the template are of the same type as those of a sample. For
instance, if the sample features are a “vector” in the mathematical sense, then the stored
template will also be a “vector”. The term “model” is used to indicate the construction of a
more complex mathematical representation capable of generating features characteristic of
a particular user. Models and features will be of different mathematical types and structures.
Models are used in some speaker and facial recognition systems. Templates are used in
fingerprint, iris, and hand geometry recognition systems.
The term “enrollment” refers to the placing of a template or model into the database
for the very first time. Once in the database and associated with an identity by external
information (provided by the enrollee or others), the enrollment biometric data is referred
to as the template or model for the individual to which it refers. The purpose of the pattern
matching process is to compare a presented feature sample to the stored data, and to send
to the decision subsystem a quantitative measure of the comparison. An exception is
enrollment in systems allowing multiple enrollments. In this application, the pattern
matching process can be skipped. In the cooperative case where the user has claimed an
identity or where there is but a single record in the current database (which might be a
magnetic stripe card), the pattern matching process might only make a comparison against
a single stored template. In all other cases, such as large-scale identification, the pattern
matching process compares the present sample to multiple templates or models from the
database one at a time, as instructed by the decision subsystem, sending on a quantitat ive
“distance” measure for each comparison. In place of a “distance”measure, some systems
use “similarity”measures, such as maximum likelihood values.
The signal processing subsystem is designed with the goal of yielding small
distances between enrolled models/templates and later samples from the same individua l
and large distances between enrolled models/templates and samples of differe nt
individuals. Even for models and samples from the same individual, however, distances
will rarely, if ever, be zero, as there will always be some non-repeatable biometric- ,
presentation-, sensor- or transmission-related variation remaining after processing. [5]
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1.4.4 Storage
The remaining subsystem to be considered is that of storage. There will be one or
more forms of storage used, depending upon the biometric system. Templates or models
from enrolled users will be stored in a database for comparison by the pattern matcher to
incoming feature samples. For systems only performing “one-to-one” matching, the
database may be distributed on smart cards, optically read cards or magnetic stripe cards
carried by each enrolled user. Depending upon system policy, no central database need
exist, although in this application a centralized database can be used to detect counterfe it
cards or to reissue lost cards without re-collecting the biometric pattern.
The database will be centralized if the system performs one-to-N matching with N
greater than one, as in the case of identification or “PIN-less verification” systems. As N
gets very large, system speed requirements dictate that the database be partitioned into
smaller subsets such that any feature sample need only be matched to the templates or
models stored in one partition, or indexed by using an appropriate data structure which
allows the templates to be visited in an advantageous order during the retrieval. These
strategies have the effect of increasing system speed and decreasing false matches, at the
expense of increasing the false non-match rate owing to partitioning errors. This means that
system error rates do not remain constant with increasing database size and identifica t ion
systems do not scale linearly. Consequently, database partitioning/indexing strategies
represent a complex policy decision.
If it may be necessary to reconstruct the biometric patterns from stored data, raw
(although possibly compressed) data storage will be required. The biometric pattern is
generally not reconstructable from the stored templates or models, although some methods
do allow a coarse reconstruction of patterns from templates. Further, the templates
themselves are created using the proprietary feature extraction algorithms of the system
vendor. The storage of raw data allows changes in the system or system vendor to be made
without the need to re-collect data from all enrolled users.
1.4.5 Decision
The decision subsystem implements system policy by directing the database search,
determines “matches” or “non-matches” based on the distance or similarity measures
received from the pattern matcher, and ultimately makes an “accept/reject” decision based
on the system policy. Such a decision policy could be to reject the identity claim (either
positive or negative) of any user whose pattern could not be acquired. For an acquired

pattern, the policy might declare a match for any distance lower than a fixed threshold and
“accept” a user identity claim on the basis of this single match, or the policy could be to
declare a match for any distance lower than a user-dependent, time-variant, or
environmentally linked threshold and require matches from multiple measures for an
“accept” decision. The policy could be to give all users, good guys and bad guys alike,
three tries to return a low distance measure and be “accepted” as matching a claimed
template. Or, in the absence of a claimed template, the system policy could be to direct the
search of all, or only a portion, of the database and return a single match or multiple
“candidate” matches. The decision policy employed is a management decision that is
specific to the operational and security requirements of the system. In general, lowering the
number of false non-matches can be traded against raising the number of false matches.
The optimal system policy in this regard depends both upon the statistical characterist ics
of the comparison distances coming from the pattern matcher, the relative penalties for
false match and false non-match within the system, and the a priori (guessed in advance)
probabilities that a user is, in fact, an impostor. In any case, in the testing of biometric
devices, it is necessary to decouple the performance of the signal processing subsystem
from the policies implemented by the decision subsystem.
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1.5 Biometrics and Privacy
1. Unlike more common forms of identification, biometric measures contain no personal
information and are more difficult to forge or steal.
2. Biometric measures can be used in place of a name or Social Security number to secure
anonymous transactions.
3. Some biometric measures (face images, voice signals and “latent” fingerprints left on
surfaces) can be taken without a person’s knowledge, but cannot be linked to an identit y
without a pre-existing invertible database.
4. A Social Security or credit card number, and sometimes even a legal name, can identify
a person in a large population. This capability has not been demonstrated using any single
biometric measure.
5. Like telephone and credit card information, biometric databases can be searched outside
of their intended purpose by court order.
6. Unlike credit card, telephone or Social Security numbers, biometric characterist ics
change from one measurement to the next.

7. Searching for personal data based on biometric measures is not as reliable or efficient as
using better identifiers, like legal name or Social Security number.
8. Biometric measures are not always secret, but are sometimes publicly observable and
cannot be revoked if compromised.
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1.6 Factors of Evaluation
1.6.1 False Accept Rate (FAR) and False Match Rate (MAR): The probability that the
system incorrectly declares a successful match between the input pattern and a
nonmatching pattern in the database. It measures the percent of invalid matches. These
systems are critical since they are commonly used to forbid certain actions by disallowed
people.
1.6.2 False Reject Rate (FRR) or False Non-Match Rate (FNMR): The probability that
the system incorrectly declares failure of match between the input pattern and the matching
template in the database. It measures the percent of valid inputs being rejected.
1.6.3 Relative Operating Characteristic (ROC): In general, the matching algorithm
performs a decision using some parameters (e.g. a threshold). In biometric systems the FAR
and FRR can typically be traded off against each other by changing those parameters. The
ROC plot is obtained by graphing the values of FAR and FRR, changing the variables
implicitly. A common variation is the Detection Error Tradeoff (DET), which is obtained
using normal deviate scales on both axes. This more linear graph illuminates the differences
for higher performances (rarer errors).
1.6.4 Equal Error Rate (EER): The rates at which both accept and reject errors are equal.
ROC or DET plotting is used because how FAR and FRR can be changed, is shown clearly.
When quick comparison of two systems is required, the ERR is commonly used. Obtained
from the ROC plot by taking the point where FAR and FRR have the same value. The lower
the EER, the more accurate the system is considered to be.
1.6.5 Failure to Enroll Rate (FTE or FER): The percentage of data input is considered
invalid and fails to input into the system. Failure to enroll happens when the data obtained
by the sensor are considered invalid or of poor quality.

1.6.6 Failure to Capture Rate (FTC): Within automatic systems, the probability that the
system fails to detect a biometric characteristic when presented correctly is generally
treated as FTC.
1.6.7 Template Capacity: It is defined as the maximum number of sets of data which can
be input in to the system.
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1.7 Biometric Applications
Biometric applications can be categorized in horizontal categories as well as vertical
markets. Biometrics are most frequently used in the following horizontal categories:
a) Citizen Identification:
Identify/authentify citizens interacting with government agencies
b) PC / Network Access:
Secure access to PCs, networks and other computer resources
c) Physical Access / Time and Attendance:
Secure access to a given area at a given time
d) Surveillance and Screening:
Identify/authentify individuals present in a given location
e) Retail / ATM / Point of Sale:
Provide identification/authentication for in-person transactions for goods/services
f) E-Commerce / Telephony:
Provide identification/authentication for remote transactions for goods/services
g) Criminal Identification:
Identify/verify individuals in law enforcement applications.
In each of those applications, biometric systems can be used to either replace or
complement existing authentication methods.
 Government Sector
 Travel and Transportation
 Financial Sector
 Health Care
 Law Enforcement

Among the technologies, finger scan is the undisputed leader with more than 50% market
share. [4]
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Chapter – 2
BIOMETRIC TECHNIQUES
There are lots of biometric techniques available nowadays. A few of them are in the
stage of the research only (e.g. the odour analysis), but a significant number of technologies
is already mature and commercially available (at least ten different types of biometrics are
commercially available nowadays: fingerprint, finger geometry, hand geometry, palm
print, iris pattern, retina pattern, facial recognition, voice comparison, signature dynamics
and typing rhythm).
2.1 Fingerprint technologies
Fingerprint identification is perhaps the oldest of all the biometric techniques.
Fingerprints were used already in the Old China as a means of positively identifying a
person as an author of the document. Their use in law enforcement since the last century is
well known and actually let to an association fingerprint = crime. This caused some worries
about the user acceptance of fingerprint-based systems. The situation improves as these
systems spread around and become more common. Systems that can automatically check
details of a person’s fingerprint have been in use since the 1960s by law enforceme nt
agencies. The U.S. Government commissioned a study by Sandia Labs to compare various
biometric technologies used for identification in early seventies. This study concluded that
the fingerprint technologies had the greatest potential to produce the best identifica t ion
accuracy. The study is quit outdated now, but it turned the research and development focus
on the fingerprint technology since its release.
Fingerprint readers
Before we can proceed any further we need to obtain the digitalized fingerprint. The
traditional method uses the ink to get the fingerprint onto a piece of paper. This piece of
paper is then scanned using a traditional scanner. This method is used only rarely today
when an old paper-based database is being digitalised, a fingerprint found on a scene of a
crime is being processed or in law enforcement AFIS systems. Otherwise modern live
fingerprint readers are used. They do not require the ink anymore. These live fingerpr int
readers are most commonly based on optical, thermal, silicon or ultrasonic principles.

Optical fingerprint readers are the most common at present. They are based on reflection
changes at the spots where the finger papilar lines touch the reader’s surface.[3]
The size of the optical fingerprint readers typically is around 10x10x5 cms. It is
difficult to minimize them much more as the reader has to comprise the source of light,
reflection surface and the light sensor.
The optical fingerprint readers work usually reliably, but sometimes have problems
with dust if heavily used and not cleaned. The dust may cause latent fingerprints, which
may be accepted by the reader as a real fingerprint. Optical fingerprint readers cannot be
fooled by a simple picture of a fingerprint, but any 3D fingerprint model makes a signific a nt
problem, all the reader checks is the pressure. A few readers are therefore equipped with
additional detectors of finger liveness.
Optical readers are relatively cheap and are manufactured by a great number of
manufacturers. The field of optical technologies attracts many newly established firms
(e.g., American Biometric Company, Digital Persona) as well as a few big and well-known
companies (such as HP, Philips or Sony). Optical fingerprint readers are also often
embedded in keyboards, mice or monitors. Silicon technologies are older than the optical
technologies. They are based on the capacitance of the finger. The dc-capacitive fingerpr int
sensors consist of rectangular arrays of capacitors on a silicon chip. One plate of the
capacitor is the finger, the other plate is a tiny area of metallization (a pixel) on the chip’s
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All the optical fingerprint readers
comprise of the source of light, the light
sensor and a special reflection surface
that changes the reflection according to
the pressure. Some of the readers are
fitted out with the processing and
memory chips as well.

surface. One places his/her finger against the surface of the chip (actually against an
insulated coating on the chip’s surface). The ridges of the fingerprint are close to the nearby
pixels and have high capacitance to them. The valleys are more distant from the pixels
nearest them and therefore have lower capacitance.
Such an array of capacitors can be placed onto a chip as small as 15 x 15x 5 mm
and thus is ideal for miniaturization. A PCMCIA card (the triple height of a credit card)
with a silicon fingerprint reader is already available. Integration of a fingerprint reader on
a credit card-sized smartcard was not achieved yet, but it is expected in the near future.
Silicon fingerprint readers are popular also in mobile phones and laptop computers due to
the small size.
Fig: Typical features Fig: Fingerprint Reader in a fingerprint
The fingerprint bitmap obtained from the silicon reader is affected by the finger
moisture as the moisture significantly influences the capacitance. This often means that too
wet or dry fingers do not produce bitmaps with a sufficient quality and so people with
unusually wet or dry fingers have problems with these silicon fingerprint readers. Both
optical and silicon fingerprint readers are fast enough to capture and display the fingerpr int
in real time. The typical resolution is around 500 DPI.
Ultrasonic fingerprint readers are the newest and least common. They use
ultrasound to monitor the finger surface. The user places the finger on a piece of glass and
the ultrasonic sensor moves and reads whole the fingerprint. This process takes one or two
seconds. Ultrasound is not disturbed by the dirt on the fingers so the quality of the bitmap
obtained is usually fair.
Fingerprint processing
Fingerprints are not compared and usually also not stored as bitmaps. Fingerprint matching
techniques can be placed into two categories: minutiae-based and correlation based.
19

Minutiae-based techniques find the minutiae points first and then map their relative
placement on the finger. Minutiae are individual unique characteristics within the
fingerprint pattern such as ridge endings, bifurcations, divergences, dots or islands (see the
picture on the following page). In the recent years automated fingerprint comparisons have
been most often based on minutiae.
The problem with minutiae is that it is difficult to extract the minutiae points
accurately when the fingerprint is of low quality. This method also does not take into
account the global pattern of ridges and furrows. The correlation-based method is able to
overcome some of the difficulties of the minutiae-based approach.However, it has some of
its own shortcomings. Correlation-based techniques require the precise location of a
registration point and are affected by image translation and rotation.
The readability of a fingerprint depends on a variety of work and environme nta l
factors. These include age, gender, occupation and race. A young, female, Asian mine-worker
is seen as the most difficult subject. A surprisingly high proportion of the population
have missing fingers, with the left forefinger having the highest percentage at 0.62%. There
are about 30 minutiae within a typical fingerprint image obtained by a live fingerpr int
reader. The number and spatial distribution of minutiae varies according to the quality of
the fingerprint image, finger pressure, moisture and placement. In the decision process, the
biometric system tries to find a minutiae transformation between the current distribut ion
and the stored template. The matching decision is then based on the possibility and
complexity of the necessary transformation. The decision usually takes from 5 milliseconds
to 2 seconds.
The speed of the decision sometimes depends on the security level and the negative
answer very often takes longer time than the positive one (sometimes even 10 times more).
There is no direct dependency between the speed and accuracy of the matching algorithm
according to our experience. We have seen fast and accurate as well as slow and less
accurate matching algorithms.
20
The minutiae matching
is a process where two
sets of minutiae are
compared to decide
whether they represent
the same finger or not.

The minutiae found in the fingerprint image are also used to store the fingerprint for future
comparisons. The minutiae are encoded and often also compressed. The size of such a
master template usually is between 24 bytes and one kilobyte. Fingerprints contain a large
amount of data. Because of the high level of data present in the image, it is possible to
eliminate false matches and reduce the number of possible matches to a small fraction. This
means that the fingerprint technology can be used for identification even within large
databases. Fingerprint identification technology has undergone an extensive research and
development since the seventies. The initial reason for the effort was the response to the
FBI requirement for an identification search system. Such systems are called Automated
Fingerprint Identification Systems (AFIS) and are used to identify individuals in large
databases (typically to find the offender of a crime according to a fingerprint found at the
crime scene or to identify a person whose identity is unknown). AFIS systems are operated
by professionals who manually intervene the minutiae extraction and matching process and
thus their results are really excellent. In today’s criminal justice applications, the AFIS
systems achieve over 98% identification rate while the FAR is below 1%.The typical access
control systems, on the other side, are completely automated. Their accuracy is slight ly
worse. The quality of the fingerprint image obtained by an automated fingerprint reader
from an unexperienced (non-professional) user is usually lower. Fingerprint readers often
do not show any fingerprint preview and so the users do not know if the positioning and
pressure of the finger is correct. The automatic minutiae extraction in a lower quality image
is not perfect yet. Thus the overall accuracy of such a system is lower.[1][3]
Some newer systems are based not only on minutiae extraction, they use the length
and position of the papilar lines as well. A few system take into account even pores (their
spatial distribution), but the problem with pores is that they are too dependent on the
fingerprint image quality and finger pressure. Most of the biometric fingerprint systems use
the fingerprint reader to provide for the fingerprint bitmap image only, whole the
processing and matching is done by a software that runs on a computer (the software is
often available for Microsoft Windows operating systems only). There are currently only
very few fingerprint devices that do all the processing by the hardware. The manufacture rs
of the fingerprint readers used to deliver the fingerprint processing software with the
hardware. Today, the market specializes. Even if it is still possible to buy a fingerpr int
reader with a software package (this is the popular way especially for the low-end devices
for home or office use) there are many manufacturers that produce fingerprint hardware
only (e.g. fingerprint silicon chips by Thomson) or software companies that offer device-
21

independent fingerprint processing software (e.g. Neuro-dynamics). Device-independe nt
software is not bound to images obtained by one single input devices, but their accuracy is
very low if various input devices are mixed.[7]
22
2.2 Iris
The iris is the coloured ring of textured tissue that surrounds the pupil of the eye.
Even twins have different iris patterns and everyone’s left and right iris is different, too.
Research shows that the matching accuracy of iris identification is greater than of the DNA
testing.[7]
The iris pattern is taken by a special gray-scale camera in the distance of 10–40 cm
from the camera (earlier models of iris scanners required closer eye positioning). The
camera is hidden behind a mirror, the user looks into the mirror so that he/she can see
his/her own eye, then also the camera can “see” the eye. Once the eye is stable (not moving
too fast) and the camera has focused properly, the image of the eye is captured (there exist
also simpler versions without auto-focus and with a capture button).
The PC iris uses a hand-held personal iris imager that functions as a computer
peripheral. The user holds the imager in his hand, looks into the camera lens from a distance
of 10 cm and presses a button to initiate the identification process. The Iris Access is more

advanced. It is auto-focus and has a sensor that checks whether an individual has stepped
in front of the camera. It is also able to guide the person audily into the correct position.
The iris scanner does not need any special lighting conditions or any special kind of light
(unlike the infrared light needed for the retina scanning). If the background is too dark any
traditional lighting can be used. Some iris scanners also include a source of light that is
automatically turned on when necessary. The iris scanning technology is not intrusive and
thus is deemed acceptable by most users. The iris pattern remains stable over a person’s
life, being only affected by several diseases. Once the gray-scale image of the eye is
obtained then the software tries to locate the iris within the image. If an iris is found then
the software creates a net of curves covering the iris. Based on the darkness of the points
along the lines the software creates the iriscode, which characterizes the iris. When
computing the iriscode two influences have to be taken into account. First, the overall
darkness of the image is influenced by the lighting conditions so the darkness threshold
used to decide whether a given point is dark or bright cannot be static, it must be
dynamically computed according to the overall picture darkness. And second, the size of
the iris dynamically changes as the size of the pupil changes. Before computing the
iriscode, a proper transformation must be done.
In the decision process the matching software given 2 iriscodes computes the
Hamming distance based on the number of different bits. The Hamming distance is a score
(within the range 0 – 1, where 0 means the same iriscodes), which is then compared with
the security threshold to make the final decision. Computing the Hamming distance of two
iriscodes is very fast (it is in fact only counting the number of bits in the exclusive OR of
the two iriscodes). Modern computers are able to compare over 4 000 000 iriscodes in one
second. The iriscode is computed very fast and takes 256 bytes. The probability that 2
different irises could produce the same iriscode is estimated as low as 1: 1078. The
probability of two persons with the same iris is very low (1:1052).
An iris scan produces a high data volume which implies a high discrimina t ion
(identification) rate. Indeed the iris systems are suitable for identification because they are
very fast and accurate. Our experience confirms all that. The iris recognition was the fastest
identification out of all the biometric systems we could work with. We have never
encountered a false acceptance (the database was rate not very large, however) and the false
rejection rate was reasonably low. The manufacturer quotes the equal error rate of
0.00008%, but so low false rejection rate is not achievable with normal (nonprofessional)
users. It is said that artificial duplication of the iris is virtually impossible because of the
23

unique properties. The iris is closely connected to the human brain and it is said to be one
of the first parts of the body to decay after death. It should be therefore very difficult to
create an artificial iris or to use a dead iris to fraudulently bypass the biometric system if
the detection of the iris liveness is working properly. We were testing an iris scanning
system that did not have any countermeasures implemented. We fooled such a system with
a very simple attack. The manufacturer provided us with a newer version of the system
after several months. We did not succeed with our simple attacks then, but we wish to note
that we did not have enough time to test more advanced versions of our attack.
Retina is not directly visible and so a coherent infrared
light source is necessary to illuminate the retina. The
infrared energy is absorbed faster by blood vessels in the
retina than by the surrounding tissue. The image of the
retina blood vessel pattern is then analysed for
characteristic points within the pattern. The retina scan is
more susceptible to some diseases than the iris scan, but
such diseases are relatively rare. [6]
24
2.3 Retina
Retina scan is based on the blood vessel pattern in the retina of the eye. Retina scan
technology is older than the iris scan technology that also uses a part of the eye. The first
retinal scanning systems were launched by EyeDentify in 1985. The main drawback of the
retina scan is its intrusiveness. The method of obtaining a retina scan is personally invasive.
A laser light must be directed through the cornea of the eye. Also the operation of the retina
scanner is not easy. A skilled operator is required and the person being scanned has to
follow his/her directions. A retina scan produces at least the same volume of data as a
fingerprint image. Thus its discrimination rate is sufficient not only for verification, but
also for identification. In the practice, however, the retina scanning is used mostly for
verification. The size of the eye signature template is 96 bytes. The retinal scanning systems
are said to be very accurate. For example the EyeDentify’s retinal scanning system has
reputedly never falsely verified an unauthorized user so far. The false rejection rate, on the
other side, is relatively high as it is not always easy to capture a perfect image of the retina.
Retinal scanning is used only rarely today because it is not user friendly and still
remains very expensive. Retina scan is suitable for applications where the high security is
required and the user’s acceptance is not a major aspect. Retina scan systems are used in

many U.S. prisons to verify the prisoners before they are released. The check of the eye
liveness is usually not of a significant concern as the method of obtaining the retina blood
vessel pattern is rather complicated and requires an operator.[2]
25
2.4 Hand geometry
Hand geometry is based on the fact that nearly every person’s hand is shaped
differently and that the shape of a person’s hand does not change after certain age. Hand
geometry systems produce estimates of certain measurements of the hand such as the length
and the width of fingers. Various methods are used to measure the hand. These methods
are most commonly based either on mechanical or optical principle. The latter ones are
much more common today. Optical hand geometry scanners capture the image of the hand
and using the image edge detection algorithm compute the hand’s characteristics. There are
basically 2 sub-categories of optical scanners. Devices from the first category create a
black-and-white bitmap image of the hand’s shape. This is easily done using a source of
light and a black-and-white camera. The bitmap image is then processed by the computer
software. Only 2D characteristics of the hand can be used in this case. Hand geometry
systems from the other category are more sophisticated. They use special guide markings
to position the hand better and have two (both vertical and horizontal) sensors for the hand
shape measurements. So, sensors from this category handle data from all the three
dimensions.

Fig: Hand Geometry Scanner
This is a hand geometry scanner HandKey II
manufactured by the Recognition systems, Inc. Special
guides use electrical conductivity to ensure that the finger s
really touch the pins. Correct position of the fingers is
indicated by a led diode on the front panel.
Hand geometry scanners are easy to use. Where the hand must be placed accurately,
guide markings have been incorporated and the units are mounted so that they are at a
comfortable height for majority of the population. The noise factors such as dirt and grease
do not pose a serious problem, as only the silhouette of the hand shape is important. The
only problem with hand geometry scanners is in the countries where the public do not like
to place their hand down flat on a surface where someone else’s hand has been placed. A
few hand geometry scanners produce only the video signal with the hand shape. Image
digitalization and processing is then done in the computer. On the other side there exist
very sophisticated and automated scanners that do everything by themselves including the
enrollment, data storage, verification and even simple networking with a master device and
multiple slave scanners. The size of a typical hand geometry scanner is considerably big
(30 x 30 x 50 cm). This is usually not a problem as the hand geometry scanners are typically
used for physical access control (e.g. at a door), where the size is not a crucial parameter.
Hand geometry does not produce a large data set (as compared to other biometric systems).
Therefore, given a large number of records, hand geometry may not be able to distinguis h
sufficiently one individual from another. The size of the hand template is often as small as
9 bytes. Such systems are not suitable for identification at all. The verification results show
that hand geometry systems are suitable for lower level security application. The hand
geometry systems are used for example at the Disney Theme Parks in the US or were used
at the 1996 Olympic Games in Atlanta. The manufacturers advertise the crossover accuracy
about 0.1%. These numbers are difficult to obtain in reality. FAR of 3% and FRR of 10%
at the middle security threshold are more realistic. The verification takes about one second.
The speed is not a crucial point because the hand geometry systems can be used for
verification only.
26
2.5 Signature dynamics
The signature dynamics recognition is based on the dynamics of making the
signature, rather than a direct comparison of the signature itself afterwards. The dynamics

is measured as a means of the pressure, direction, acceleration and the length of the strokes,
number of strokes and their duration. The most obvious and important advantage of this is
that a fraudster cannot glean any information on how to write the signature by simply
looking at one that has been previously written.
Pioneers of the signature verification first developed a reliable statistical method in
1970s. This involved the extraction of ten or more writing characteristics such as the
number of times the pen was lifted, the total writing time and the timing of turning points.
The matching process was then performed using fairly standard statistical correlation
methods. Newer sequential techniques treat the signature as a number of separate events,
with each event consisting of the period between the pen striking the writing surface and
lifting off again. This approach is much more flexible. If the majority of the signature is
accurate and only one event is missing or added then this event can be easily ignored.
There are various kinds of devices used to capture the signature dynamics. These
are either traditional tablets or special purpose devices. Tablets capture 2D coordinates and
the pressure. Special pens are able to capture movements in all 3 dimensions.
Tablets have two significant disadvantages. First, the resulting digitalised signature
looks different from the usual user signature. And second, while signing the user does not
see what he/she has written so far. He/she has to look at the computer monitor to see the
signature. This is a considerable drawback for many (unexperienced) users. Some special
pens work like normal pens, they have ink cartridge inside and can be used to write with
them on paper. [6]
Fig: (a) E-pad (b) SmartPen
These are special purpose devices used to capture the signature dynamics. Both are
wireless. The E-pad devices shows the signature on the digital display while the
Smartpen has got its own ink cartridge and can be used to write onto any paper.
A person does not make a signature consistently the same way, so the data obtained
from a signature from a person has to allow for quite some variability. Most of the signature
dynamics systems verify the dynamics only, they do not pay any attention to the resulting
27

signature. A few systems claim to verify both (i.e. the signature dynamics as well as the
resulting signature look itself). Our experience shows that if the system does not verify the
resulting signature, then the signature that is accepted as a true match may look significant ly
different from the master template. The speed of writing is often the most important factor
in the decision process, so it is possible to successfully forge a signature even if the resulting
signature looks so different that any person would notice. We have tried simple attempts to
sign as other users as well as simulation of attacks where the attacker has seen a user signing
once or several times. Our results show that individuals’ ability to fake signature dynamics
substantially improves after they see the way the true signers sign.
The size of data obtained during the signing process is around 20 KB. The size of
the master template, which is computed from 3 to 10 signatures, varies from around 90
bytes up to a few kilobytes. Even if the size of the master template is relatively high the
signature recognition has problems with match discrimination and thus is suitable for
verification only. The accuracy of the signature dynamics biometric systems is not high,
the crossover rate published by manufacturers is around 2%, but according to our own
experience the accuracy is much worse. The leading companies in the signature systems
are Cyber-Sign, PenOp and Quintet.
28
2.6 Facial recognition
Facial recognition is the most natural means of biometric identification. The method
of distinguishing one individual from another is an ability of virtually every human. Until
recently the facial recognition has never been treated as a science.
Any camera (with a sufficient resolution) can be used to obtain the image of the
face. Any scanned picture can be used as well. Generally speaking the better the image
source (i.e. camera or scanner) the more accurate results we get. The facial recognit ion
systems usually use only the gray- scale information. Colours (if available) are used as a
help in locating the face in the image only. The lighting conditions required are mainly
dependent on the quality of the camera used. In poor light condition, individual features
may not be easily discernible. There exist even infrared cameras that can be used with facial
recognition systems.
Most of facial recognition systems require the user to stand a specific distance away
from the camera and look straight at the camera. This ensures that the captured image of

the face is within a specific size tolerance and keeps the features (e.g., the eyes) in as similar
position each time as possible.
The first task of the processing software is to locate the face (or faces) within the
image. Then the facial characteristics are extracted. Facial recognition technology has
recently developed into two areas: facial metrics and eigenfaces. Facial metrics technology
relies on the measurement of the specific facial features (the systems usually look for the
positioning of the eyes, nose and mouth and the distances between these features). Another
method for facial recognition has been developed in the past three years. The method is
based on categorizing faces according to the degree of fit with a fixed set of 150 master
eigenfaces. This technique is in fact similar to the police method of creating a portrait, but
the image processing is automated and based on a real picture here. Every face is assigned
a degree of fit to each of the 150 master eigenfaces, only the 40 template eigenfaces with
the highest degree of fit are necessary to reconstruct the face with the accuracy of 99%.
The image processing and facial similarity decision process is done by the computer
software at the moment, this processing requires quite a lot of computing power and so it
is not easy to assemble a stand-alone device for face recognition. There are some efforts
(by companies like Siemens) to create a special-purpose chip with embedded face
recognition instruction set.
The accuracy of the face recognition systems improves with time, but it has not
been very satisfying so far. According to our experience there is still a potential for
improving the algorithms for face location. The current software often does not find the
face at all or finds “a face” at an incorrect place. This significantly makes the results worse.
Better results can be achieved if the operator is able to tell the system exactly where the
eyes are positioned. The systems also have problems to distinguish very similar persons
like twins and any significant change in hair or beard style requires re-enrollment. Glasses
can also cause additional difficulties. The quoted accuracy of facial recognition systems
varies significantly, many systems quote the crossover accuracy of less than one percent.
The numbers from real systems are not so pleasant, the crossover accuracy is much higher
and indicates that these systems are not suitable for identification. If security is the main
concern then even the verification accuracy may not be sufficiently good. Facial
recognition systems are offered by a great number of suppliers nowadays, to name a few
of them: Miros, Neurodynamics or Visionics.[7]
The face recognition system does not require any contact with the person and can
be fooled with a picture if no countermeasures are active. The liveness detection is based
29

most commonly on facial mimics. The user is asked to blink or smile. If the image changes
properly then the person is considered “live”. A few systems can simultaneously process
images from two cameras, from two different viewpoints. The use of two cameras can also
avoid fooling the system with a simple picture. [5]
Extraction of face
30
Input face
image
2.7 Speaker verification
Extraction of face
features
Database
image
Positive
Negative
The principle of speaker verification is to analyse the voice of the user in order to
store a voiceprint that is later used for identification/ verification. Speaker verification and
speech recognition are two different tasks. The aim of speech recognition is to find what
has been told while the aim of the speaker verification is who told that. Both these
technologies are at the edge between research and industrial development. Texas
Instruments reported their work in speech verification for access control already in the early
1970’s.There are many commercial systems available today, but their accuracy still can be
improved.
Speaker verification focuses on the vocal characteristics that produce speech and
not on the sound or the pronunciation of the speech itself. The vocal characteristics depend
on the dimensions of the vocal tract, mouth, nasal cavities and the other speech processing
mechanisms of the human body.
The greatest advantage of speaker verification systems is that they do not require
any special and expensive hardware. A microphone is a standard accessory of any
multimedia computer, speaker verification can also be used remotely via phone line. A high

sampling rate is not required, but the background (or network) noise causes a signific a nt
problem that decreases the accuracy. The speaker verification is not intrusive for users and
is easy to use.
The system typically asks the user to pronounce a phrase during the enrollment, the
voice is then processed and stored in a template (voiceprint). Later the system asks for the
same phrase and compares the voiceprints. Such a system is vulnerable to replay attacks; if
an attacker records the user’s phrase and replays it later then he/she can easily gain the
user’s privilege. More sophisticated systems use a kind of challenge-response protocol.
During the enrolment the system records the pronunciation of multiple phrases (e.g.
numbers). In the authentication phase the system randomly chooses a challenge and asks
the user to pronounce it. In this case the system not only compares the voiceprints, but also
deploys the speech recognition algorithms and checks whether the proper challenge has
really been said. There exist (very few) systems that are really text independent and can
cope with the full vocabulary.
Speaker verification is quite secure from the professional mimics since the system
make a comparison of the word stored in a different way than humans compare voices.
Currently there are three major international projects in the field of voice technology:
PICASSO, CASCADE and Cost 250. There is a great number of commercially available
voice systems as well. Keyware, VeriTel and International Electronics are a few of the
leading companies. Speaker verification is a biometric technique based on behavioura l
characteristic and as such can be negatively affected by the current physical condition and
the emotional state. The accuracy of the speaker verification can also be affected by the
background and network noise in the input signal. This increases the false rejection rate.
During the tests of a speaker verification system in the Sandia Labs the false acceptance
rate after a single attempt was 0.9% and the false rejection rate after three attempts was
4.3%. A trial at UBS’s Ubilab achieved the equal error rate of 0.16% after a one attempt.
31
2.8 Other biometric techniques
2.8.1 Palmprint
Palmprint verification is a slightly different implementation of the fingerpr int
technology. Palmprint scanning uses optical readers that are very similar to those used for
fingerprint scanning, their size is, however, much bigger and this is a limiting factor for the
use in workstations or mobile devices.

32
2.8.2 Hand vein
Hand vein geometry is based on the fact that the vein pattern is distinctive for
various individuals. The veins under the skin absorb infrared light and thus have a darker
pattern on the image of the hand taken by an infrared camera. The hand vein geometry is
still in the stage of research and development. One such system is manufactured by Britis h
Technology Group. The device is called Veincheck and uses a template with the size of 50
bytes.
2.8.3 DNA
DNA sampling is rather intrusive at present and requires a form of tissue, blood or
other bodily sample. This method of capture still has to be refined. So far the DNA analysis
has not been sufficiently automatic to rank the DNA analysis as a biometric technology.
The analysis of human DNA is now possible within 10 minutes. As soon as the technology
advances so that DNA can be matched automatically in real time, it may become more
significant. At present DNA is very entrenched in crime detection and so will remain in the
law enforcement area for the time being.
2.8.4 Thermal imaging
This technology is similar to the hand vein geometry. It also uses an infrared source
of light and camera to produce an image of the vein pattern in the face or in the wrist.
2.8.5 Ear shape
Identifying individuals by the ear shape is used in law enforcement applications
where ear markings are found at crime scenes. Whether this technology will progress to
access control applications is yet to be seen. An ear shape verifier (Optophone) is produced
by a French company ART Techniques. It is a telephone type handset within which is a
lighting unit and cameras which capture two images of the ear.
2.8.6 Body odour
The body odour biometrics is based on the fact that virtually each human smell is
unique. The smell is captured by sensors that are capable to obtain the odour from non-intrusive
parts of the body such as the back of the hand. Methods of capturing a person’s
smell are being explored by Mastiff Electronic Systems. Each human smell is made up of
chemicals known as volatiles. They are extracted by the system and converted into a

template. The use of body odour sensors brings up the privacy issue as the body odour
carries a significal amount of sensitive personal information. It is possible to diagnose some
diseases or activities in the last hours (like sex, for example) by analysing the body odour.
33
2.8.7 Keystroke dynamics
Keystroke dynamics is a method of verifying the identity of an individual by their
typing rhythm which can cope with trained typists as well as the amateur two-finger typist.
Systems can verify the user at the log-on stage or they can continually monitor the typist.
These systems should be cheap to install as all that is needed is a software package.
2.8.8 Fingernail bed
The US Company AIMS is developing a system which scans the dermal structure
under the fingernail. This tongue and groove structure is made up of nearly parallel rows
of vascular rich skin. Between these parallel dermal structures are narrow channels, and it
is the distance between these which is measured by the AIMS system.

34
Chapter – 3
PRACTICAL ISSUES
3.1 The Core Biometric Technology
There are at least ten biometric techniques commercially available and new
techniques are in the stage of research and development. What conditions must be fulfilled
for a biological measurement to become a biometric? Any human physiological or
behavioural characteristics can become a biometric provided the following properties are
fulfilled.
* Universality: This means that every person should have the characteristics. It is really
difficult to get 100% coverage. There are mute people, people without fingers or with
injured eyes. All these cases must be handled.
* Uniqueness: This means that no two persons should be the same in terms of the biometric
characteristics. Fingerprints have a high discrimination rate and the probability of two
persons with the same iris is estimated as low as 1:1052. Identical twins, on the other side,
cannot be easily distinguished by face recognition and DNA-analysis systems.
* Permanence: This means that the characteristics should be invariant with time. While
the iris usually remains stable over decades, a person’s face changes significantly with time.
The signature and its dynamics may change as well and the finger is a frequent subject to
injuries.
* Collectability: This means that the characteristics must be measured quantitatively and
obtaining the characteristics should be easy. Face recognition systems are not intrusive and
obtaining of a face image is easy. In the contrast the DNA analysis requires a blood or other
bodily sample. The retina scan is rather intrusive as well.
* Performance: This refers to the achievable identification/verification accuracy and the
resources and working or environmental conditions needed to achieve an acceptable
accuracy. The crossover accuracy of iris-based systems is under 1% and the system is able

to compare over 4 X 106 iriscodes in one second. The crossover accuracy of some signature
dynamics systems is as high as 25% and the verification decision takes over one second.
* Acceptability: This indicates to what extend people are willing to accept the biometric
system. Face recognition systems are personally not intrusive, but there are countries where
taking pictures of persons is not viable. The retina scanner requires an infrared laser beam
directed through the cornea of the eye. This is rather invasive and only few users accept
this technology.
* Circumvention: This refers to how difficult it is to fool the system by fraudule nt
techniques. An automated access control system that can be easily fooled with a fingerpr int
model or a picture of a user’s face does not provide much security. [7]
35
3.2 The layer model
Although the use of each biometric technology has its own specific issues, the basic
operation of any biometric system is very similar. The system typically follows the same
set of steps. The separation of actions can lead to identifying critical issues and to
improving security of the overall process of biometric authentication. The whole process
starts with the enrollment:
First measurement (acquisition)
This is the first contact of the user with the biometric system. The user’s biometric
sample is obtained using an input device. The quality of the first biometric sample is crucial
for further authentications of the user, so the quality of this biometric sample must be
particularly checked and if the quality is not sufficient, the acquisition of the biometric
sample must be repeated. It may happen that even multiple acquisitions do not generate
biometric samples with sufficient quality. Such a user cannot be registered with the system.
There are also mute people, people without fingers or with injured eyes. Both these
categories create a ”failed to enroll“ group of users. Users very often do not have any
previous experiences with the kind of the biometric system they are being registered with,
so their behaviour at the time of the first contact with the technology is not natural. This
negatively influences the quality of the first measurement and that is why the first
measurement is guided by a professional who explains the use of the biometric reader.

36
Creation of master characteristics
The biometric measurements are processed after the acquisition. The number of
biometric samples necessary for further processing is based on the nature of the used
biometric technology. Sometimes a single sample is sufficient, but often multiple (usually
3 or 5) biometric samples are required. The biometric characteristics are most commonly
neither compared nor stored in the raw format (say as a bitmap). The raw measurements
contain a lot of noise or irrelevant information, which need not be stored. So the
measurements are processed and only the important features are extracted and used. This
significantly reduces the size of the data. The process of feature extraction is not lossless
and so the extracted features cannot be used to reconstruct the biometric sample completely.
Storage of master characteristics
After processing the first biometric sample and extracting the features, we have to
store (and maintain) the newly obtained master template. Choosing a proper discrimina t ing
characteristic for the categorization of records in large databases can improve identifica t io n
(search) tasks later on. There are basically 4 possibilities where to store the template: in a
card, in the central database on a server, on a workstation or directly in an authentica t ion
terminal.
The storage in an authentication terminal cannot be used for large-scale systems, in
such a case only the first two possibilities are applicable. If privacy issues need to be
considered then the storage on a card has an advantage, because in this case no biometric
data must be stored (and potentially misused) in a central database. The storage on a card
requires a kind of a digital signature of the master template and of the association of the
user with the master template. Biometric samples as well as the extracted features are very
sensitive data and so the master template should be stored always encrypted no matter what
storage is used. As soon as the user is enrolled, he/she can use the system for successful
authentications or identifications. This process is typically fully automated and takes the
following steps:
Acquisition(s)
The current biometric measurements must be obtained for the system to be able to
make the comparison with the master template. These subsequent acquisitions of the user’s
biometric measurements are done at various places where the authentication of the user is
required. This might be user’s computer in the office, an ATM machine or a sensor in front

of a door. For the best performance the kind of the input device used at the enrollment and
for the subsequent acquisitions should be the same. Other conditions of use should also be
as similar as possible with the conditions at the enrollment. These includes the background
(face recognition), the background noise (voice verification) or the moisture (fingerprint).
While the enrollment is usually guided by trained personnel, the subsequent biometric
measurements are most commonly fully automatic and unattended. This brings up a few
special issues. Firstly, the user needs to know how to use the device to provide the sample
in the best quality. This is often not easy because the device does not show any preview of
the sample obtained, so for example in the case of a fingerprint reader, the user does not
know whether the positioning of the finger on the reader and the pressure is correct.
Secondly, as the reader is left unattended, it is up to the reader to check that the
measurements obtained really belong to a live persons (the liveness property). For example,
a fingerprint reader should tell if the fingerprint it gets is from a live finger, not from a mask
that is put on top of a finger. Similarly, an iris scanner should make sure that the iris image
it is getting is from a real eye not a picture of an eye. In many biometric techniques (e.g.
fingerprinting) the further processing trusts the biometric hardware to check the liveness of
the person and provide genuine biometric measurements only. Some other systems (like
the face recognition) check the user’s liveness in software (the proper change of a
characteristic with time). No matter whether hardware or software is used, ensuring that the
biometric measurements are genuine is crucial for the system to be secure. Without the
assumption of the genuine data obtained at the input we cannot get a secure system. It is
not possible to formally prove that a reader provides only genuine measurements and this
affects also the possibility of a formal proof of the security of whole the biometric system.
The liveness test of a person is not an easy task. New countermeasures are always to be
followed by newer attacks. We do not even know how efficient the current countermeasures
are against the attacks to come. Biometric readers are not yet the main target of
sophisticated criminals. But then we can expect a wave of professional attacks. We have
seen a few biometric readers where the estimated cost of an attack is as low as a few
hundred dollars. The security of such a system is really poor.[3]
37
Creation of new characteristics
The biometric measurements obtained in the previous step are processed and new
characteristics are created. The process of feature extraction is basically the same as in the
case of the enrollment. Only a single biometric sample is usually available. This might

mean that the number or quality of the features extracted is lower than at the time of
enrollment.
38
Comparison
The currently computed characteristics are then compared with the characterist ics
obtained during enrollment. This process is very dependent on the nature of the biometric
technology used. Sometimes the desired security threshold is a parameter of the matching
process, sometimes the biometric system returns a score within a range. If the system
performs verification then the newly obtained characteristics are compared only with one
master template (or with a small number of master templates, e.g. a set of master templates
for a few different fingers). For an identification request the new characteristics are
matched against a large number of master templates (either against all the records in the
database or if the database is clustered then against the relevant part of the database)
Decision
The final step in the verification process is the yes/no decision based on the
threshold. This security threshold is either a parameter of the matching process or the
resulting score is compared with the threshold value to make the final decision. In the case
of identification the user whose master template exceeds the threshold is returned as the
result. If multiple master templates exceed the threshold then either all these users are
returned as the result or the template with the highest score is chosen. Although the error
rates quoted by manufactures (typically ERR < 1%) might indicate that biometric systems
are very accurate, the reality is rather different.
The accuracy of biometric systems used by non-professional users is much lower.
Especially the false rejection rate is in reality very high (very often over 10%). This
prevents the legitimate users to gain their access rights and stands for a significant problem
of the biometric systems.
3.3 Biometrics and cryptography
Is cryptography necessary for the secure use of biometric systems?
The answer is quite clear: Yes.
There are basically two kinds of biometric systems:

* Automated identification systems operated by professionals. The purpose of such systems
is to identify an individual in question or to find an offender of a crime according to trails
left on the crime scene. The operators of these systems do not have any reason to cheat the
system, so the only task for the cryptography is to secure the sensitive biometric data.
* Access control systems. These systems are used by ordinary users to gain a privilege or
an access right. Securing such a system is much more complicated task. Let us consider
further the general-use systems of the latter type, as this report is devoted solely to the use
of biometrics for the authentication.
39
Biometrics are not secrets
Some systems incorrectly assume that biometric measurements are secret and grant
access when matching biometric measurements are presented. Such systems cannot cope
with the situations when the biometric measurements are disclosed, because the biometr ics
cannot be changed (unless the user is willing to have an organ transplant). Moreover, the
user will not learn that his/her biometric is disclosed. People leave fingerprints on
everything they touch, and the iris can be observed anywhere they look. Biometr ics
definitely are sensitive data and therefore should be properly protected, but they cannot be
considered secret. So the security of the system cannot be based on knowledge of the
biometric characteristics. When using secret keys or passwords for authentication, a
common method to defeat replay attacks is to use a challenge-response protocol, in which
the password is never transmitted. Instead, the server sends a challenge that can only be
answered correctly if the client knows the correct password. Unfortunately, this method
does not apply to biometric data. The difference between a password and a fingerprint is
that the password is supposed to be secret, while the fingerprint is not.[1]
Hence, replaying attacks are inherent with biometric authentication schemes. The
only way how to make a system secure is to make sure that the characteristics presented
came from a real person and were obtained at the time of verification.
The liveness problem
So-called liveness problem is a closely related issue. One has to make sure that the
authentication device is verifying a live person. The liveness test is dependent on the kind
of biometric technology used and it is a task left up to the core biometric technology. Some
biometric techniques (e.g. face recognition or voice verification) may use experiences with

the challenge-response protocols used in cryptography. The user is then asked to pronounce
a randomly chosen phrase or make a certain movement. The biometric system has to trust
the input device it provides only genuine measurements. We cannot make a secure system
if we do not trust the biometric input device. If a malicious party can easily tamper with a
fingerprint scanner, the whole system is not secure no matter how secure the other parts of
the system are. In terms of the hardware of the device, until now, only smartcard-based
devices can provide certain level of tamper-resistance. (Note: Smartcards are hardly ever
tamper-proof, rather tamper-resistant.) The trustworthiness of a device is also a relative
concept that depends on how the device is used. For example, a removable optical finger
scanner put in a public place may be treated as untrustworthy, while the same removable
optical finger scanner may be treated as trustworthy in a place where there is a constant
human supervision. [1]
40
Authentication software
The biometric system must be convinced that the presented biometric
measurements come from a trusted input device and were captured at a certain time. If the
authentication is done on-device, the device itself should be trustworthy. If the
authentication is done off-device, then the operating environment of the software and the
communication link between the software and the device, have to be secure. For example,
in a client-server application, if the client workstation is not trusted, then there is no point
authenticating a user using that workstation. If one chooses to run the authentica t ion
software at the server side, then the communication link between the server and the device
itself (not just the client workstation) has to be secured. Otherwise, a malicious party or
even the workstation itself may intercept the communication and replay recorded biometric
data. One way to defeat replaying attacks is to put a separate secret key in the device and
use challenge/response protocol with this key. Obviously, the device has to be trustworthy.
The best solution probably is to use a TLS-like protocol with mandatory authentication of
both parties. In any case it is necessary to transmit the whole biometric measurements over
the connection.
Either the reader sends the biometric measurements to the workstation (or server or
whatever grants the access right) to make the match or the workstation provides the master
template to the reader that makes the matching. Hashing in the usual sense and sending
only the hash over the link does not help here, because the biometric measurements never
are the same. To make it work we either would have to ensure that the biometric

measurements are always the same (but see the warning below) or change the hash function
not to depend on all the input. One has to consider that 100% similarity of two samples
from different biometric measurements implies a good forgery. This is true with almost
100% probability.
41
Improving security with biometrics
Can biometrics help cryptography to increase the security? Here the answer is not
so clear. Cryptography has been relatively successfully used without biometrics over
decades. But it still can benefit from the use of biometrics. To put it simple, cryptography
is based on keys. Secure storage of keys is a crucial non-trivial task. Key management often
is the weakest point of many systems. Secret and private keys must be kept secret, and here
the biometric technologies might help. Indeed, one of the most promising applications of
biometrics is the secret key protection. If a user’s local workstation is trusted, then the
problem of the authentication software is minor, but the input device must be trustworthy.
The security concerns are the same no matter whether the secret (or private) keys are stored
on a smart-card or on the hard disk of the workstation. If a user’s workstation is not trusted,
the private keys have to be stored in a separate secure place, usually a smartcard. Smartcard
based solutions where the secret key is unlocked only after a successful biometric
verification increase the overall security, as the biometric data does not need to leave the
card. For smartcards the fingerprint techniques with a silicon fingerprint reader are most
commonly used today. It is necessary to distinguish securing a key with biometrics and
generating a key from biometrics. The latter does not work. It must be pointed out that
biometric data cannot be used as capability tokens in the same way as secret keys or
passwords. In secret key or password based access control schemes, a key/password itself
can be used as a capability. Knowing a secret key or a password can mean that the user has
the right to use certain application. However, this does not apply to biometric data. As we
already know biometrics are not secrets. One viable way is to use digital certificates. Digita l
certificates can be used as capabilities or digital identities that allow users to access remote
applications, while biometrics is used to secure the access/usage of the private keys
associated with the digital certificates.[4]

42
Chapter – 4
CONCLUSION
Even if the accuracy of the biometric techniques is not perfect yet, there are many
mature biometric systems available now. Proper design and implementation of the
biometric system can indeed increase the overall security, especially the smartcard based
solutions seem to be very promising. Making a secure biometric systems is, however, not
as easy as it might appear. The word biometrics is very often used as a synonym for the
perfect security. This is a misleading view. There are numerous conditions that must be
taken in account when designing a secure biometric system. First, it is necessary to realize
that biometrics are not secrets. This implies that biometric measurements cannot be used as
capability tokens and it is not secure to generate any cryptographic keys from them. Second,
it is necessary to trust the input device and make the communication link secure. Third, the
input device needs to check the liveness of the person being measured and the device itself
should be verified for example by a challenge-response protocol.

REFERENCES
[1] C. Calabrese: The trouble with biometrics, ;login:, Volume 24, Number 4
[2] EyeDentify, http://www.eyedentify.com/
[3] A. Jain et al: BIOMETRICS: Personal Identification in Networked Society, Kluwer
Academic Publishers, 1999, ISBN 0-7923-8345-1
[4] E. Newham, The biometric report, SBJ Services, 1995
[5] Pattern Recognition and Image Processing Lab, Michigan State University,
http://biometrics.cse.msu.edu/
[6] Biometric Authentication: A Review, International Journal of u- and e- Service, Science
and Technology Vol. 2, No. 3, September, 2009
[7] Unique Identification, http://uidai.gov.in/authentication-overview.html
43

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