Biometrics-India

A.M.Jain Institute of Management

2006-08-01T21:55:00.000-07:00

A.M.Jain Institute of Management: "A.M.Jain Institute of Management"

Signature recognition

2005-06-25T04:50:00.000-07:00

Signature verification is the process used to recognize an individual’s hand-written signature.

Dynamic signature verification technology uses the behavioral biometrics of a hand written signature to confirm the identity of a computer user. This is done by analyzing the shape, speed, stroke, pen pressure and timing information during the act of signing. Natural and intuitive, the technology is easy to explain and trust.

As a replacement for a password or a PIN number, dynamic signature verification is a biometric technology that is used to positively identify a person from their handwritten signature.

There is an important distinction between simple signature comparisons and dynamic signature verification. Both can be computerized, but a simple comparison only takes into account what the signature looks like. Dynamic signature verification takes into account how the signature was made. With dynamic signature verification it is not the shape or look of the signature that is meaningful, it is the changes in speed, pressure and timing that occur during the act of signing. Only the original signer can recreate the changes in timing and X, Y, and Z (pressure).

A pasted bitmap, a copy machine or an expert forger may be able to duplicate what a signature looks like, but it is virtually impossible to duplicate the timing changes in X, Y and Z (pressure). The practiced and natural motion of the original signer would required to repeat the patterns shown.

There will always be slight variations in a person’s handwritten signature, but the consistency created by natural motion and practice over time creates a recognizable pattern that makes the handwritten signature a natural for biometric identification.

Signature verification is natural and intuitive. The technology is easy to explain and trust. The primary advantage that signature verification systems have over other types of biometric technologies is that signatures are already accepted as the common method of identity verification. This history of trust means that people are very willing to accept a signature based verification system.

Dynamic signature verification technology uses the behavioral biometrics of a hand written signature to confirm the identity of a computer user. Unlike the older technologies of passwords and keycards - which are often shared or easily forgotten, lost, and stolen - dynamic signature verification provides a simple and natural method for increased computer security and trusted document authorization.

Signature-Scan: How It Works

Signature-scan technology utilizes the distinctive aspects of the signature to verify the identity of individuals. The technology examines the behavioral components of the signature, such as stroke order, speed and pressure, as opposed to comparing visual images of signatures. Unlike traditional signature comparison technologies, signature-scan measures the physical activity of signing. While a system may also leverage a comparison of the visual appearance of a signature, or “static signature,” the primary components of signature-scan are behavioral.

The signature, along with the variables present during the signing process, is transmitted to a local PC for template generation. Verification can take place against a local PC or a central PC, depending on the application. In employee-facing signature-scan applications such as purchase order authentication, local processing may be preferred; there may be just a single PC used for such authorization. For customer-facing applications, such as retail or banking authentication, centralized authentication is likely necessary because the user may sign at one of many locations.

The results of signature-scan comparisons must be tied into existing authentication schemes or used as the basis of new authentication procedures. For example, in a transactional authentication scenario, the “authorize transaction” message might be sent after a signature is acquired by a central PC. When signature-scan is integrated into this process, an additional routine requires that the signature characteristics be successfully matched against those on file in order for the “authorize transaction” message to go forward. In other applications, the results of a signature-scan match may simply be noted and appended to a transaction. For example, in document authentication, an unsuccessful comparison may be flagged for future resolution while not halting a transaction. The simplest example would be a signature used for handheld device login: the successful authentication message merely needs to be integrated into the login module, similarly to a PIN or password

Signature-Scan: Strengths and Weaknesses

Signature-Scan has several strengths. Because of the large amount of data present in a signature-scan template, as well as the difficulty in mimicking the behavior of signing, signature scan-technology is highly resistant to imposter attempts. As a result of the low False Acceptance Rates (FAR), a measure of the likelihood that a user claiming a false identity will be accepted, deployers can have a high confidence level that successfully matched users are who they claim to be. Signature-scan also benefits from its ability to leverage existing processes and hardware, such as signature capture tablets and systems based on public key infrastructure (PKI), a popular method for data encryption. Since most people are accustomed to providing their signatures during customer interactions, the technology is considered less invasive than some other biometrics.

However, signature-scan has several weaknesses. Signature-scan is designed to verify subjects based on the traits of their unique signature. As a result, individuals who do not sign their names in a consistent manner may have difficulty enrolling and verifying in signature-scan. During enrollment subjects must provide a series of signatures that are similar enough that the system can locate a large percentages of the common characteristics between the enrollment signatures. During verification enough characteristics must remain constant to determine with confidence that the authorized person signed. As a result, individuals with muscular illnesses and people who sometimes sign with only their initials might result in a higher False Rejection Rate (FRR), which measures the likelihood that a system will incorrectly reject an authorized user. Since many users are unaccustomed to signing on a tablet, some subjects' signatures may differ to their signatures on ink and paper, increasing the potential for false rejection.

Typical Signature-Scan Applications

Signature-scan is implemented in situations where signature or written input processes are already in place. These applications include contract execution, formal agreements, acknowledgement of services received, access to controlled documents, etc.

As the act of signing documents becomes more integrated with electronic capture processes - signing on acquisition tablets, using special styluses, etc. - the opportunity for biometric authentication will increase dramatically. As of today, there are few acquisition devices deployed in operational environments capable of capturing biometric data. Note that signature-scan is not the same as signature capture, currently used in various point-of-sale systems. Nor is it the same as digital signatures, an encryption technology.

Signature-Scan Market Size

Though it is one of the least frequently deployed technology in the biometric market today, signature-scan usage will increase, as a complement to static signature capture, through 2005. Though a handful of vendors sell signature-scan, these firms will need to show the success of the technology in more high-profile settings. As applications for contract execution, formal agreements and access to controlled documents are demonstrated, signature-scan revenues are projected to grow from $3.0m in 2000 to $101.1m in 2005. Signature-scan revenues are expected to comprise approximately 5% of the entire biometric market.

Difference between Biometric and digital signatures

Handwriting has been around since the beginning of civilization and the ‘signature ’or the act of sign-ing a document,has long been accepted by nearly every culture as one ’s recognition and agreement on the contents and implications of written words.

The increasing recognition of electronic signatures by lawmakers is bringing to the forefront concerns over electronic security for privacy and protection of individuals.

For many now conducting business transactions over private networks or the Internet,some form of offcial acknowledgement is now essential and legally binding.The security implications of producing or recognizing ‘original ’electronic documents will be more important than ever before.In this respect, it is important to understand the distinction between the terms “Biometric ” and “Digital ” signatures.

A digital signature is a term used to describe a long numerical code that has been uniquely assigned to one person,hence the reference to ‘signature ’.It has nothing to do with a real signature.Their purpose is to be used in encryption systems.Asymmetric encryption (or PKI)is an example of a popular encryption approach.A digital signature is issued to an individual by what is called a Certificate Authority.This is a group or organization responsible for maintenance and safekeeping of digital signatures.Because of their length no one actually remembers or even knows their digital signature.

An individual ’s digital signature will normally reside on his or her computer,or can be stored on a card (similar to banking cards).When someone wishes to encrypt an electronic document,they will use a password or PIN that in turn allows the digital signature to be used.Although secure once encrypted, digital signatures are only as safe as the medium where they reside.Anyone obtaining access to your password,PIN or computer can potentially make unauthorized use of your digital signature.The use of a digital signature does not guarantee the identity of the originator. Handwriting results from a highly complex series of dynamic neuromuscular tasks from brain to fingertips.A naturally developed signature represents the most often reproduced and habitual act of writing.

Although we never sign exactly the same way twice,the signature adheres within certain boundaries unique to each individual.This natural variation is an essential component of handwriting.It also means that each signature is unique in that no two will be identical in all discrete features.Unlike fingerprints,retinal or DNA patterns which remain constant over time,the execution of a person ’s signature will be unique and individual at that particular moment.Handwriting remains one of the most powerful human identifiers that exist today.Identical twins will have the same DNA pattern while their handwriting and signatures remain distinctively different.

Biometric signature is a term used to refer to a signature that has been recorded/captured using a variety of input devices such as digitizing tablets,personal digital assistants (PDA),computer displays or other contact sensitive technologies.This method allows real handwritten signatures to be incorporated into e-documents during electronic transactions.Not every technology captures signature information the same way.Some systems have a static approach and will only record an image of a signature and as such do not record the unique behavioral elements associated with the execution of a signature.In a biometric system such as CIC ’s SignItÔ,both the geometric and dynamic characteristics of the signing process will be recorded and incorporated in an electronic document.Most of the elements that make a signature unique and identifiable can be derived from the digital signature data.Furthermore,the data that is incorporated in an electronic document can be used to lock and protect the contents from alteration.Biometric signatures can also be used to provide and control access security to buildings,networks,computers,documents and databases.

For the layperson,the pictorial appearance of a conventional signature can be convincingly imitated. Forensically,when there is a question of whether or not the signature on a document is genuine, expert visual and microscopic examination is required.This involves evaluating and comparing the general and discrete features of the contested signature with known signatures.With biometric signatures,the authentication can be done in real-time or after the fact.In the event that a biometric signature is contested,the signature data can be extracted from the document and submitted to similar forensic investigation and analysis to verify the authenticity of the signature.

In fact, some of the biometric data that is captured such as speed,acceleration,deceleration,and the amount of time the pen is on and off the paper is accurately measured.This data is either unavailable or qualitatively assessed at best in conventional forensic examinations of signatures.The additional behavioral features recorded from biometric signatures make them even more difficult if not impossible to imitate.

Biometric signatures represent an ideal bridge between the long-recognized convention of signing a document and the need for electronic documents to be uniquely recognized by individuals.This application provides individuals with security and control on documents originated,transacted and stored in the digital domain.

Iris recognition

2005-06-25T04:44:00.000-07:00

Iris scan biometrics employs the unique characteristics and
features of the human iris in order to verify the identity of
an individual. The iris is the area of the eye where the
pigmented or coloured circle, usually brown or blue, rings the
dark pupil of the eye.

The iris-scan process begins with a photograph. A
specialized camera, typically very close to the subject, no
more than three feet, uses an infrared imager to illuminate
the eye and capture a very high-resolution photograph. This
process takes only one to two seconds and provides the details
of the iris that are mapped, recorded and stored for future
matching/verification.

Eyeglasses and contact lenses present no problems to the
quality of the image and the iris-scan systems test for a live
eye by checking for the normal continuous fluctuation in pupil
size.

The inner edge of the iris is located by an iris-scan
algorithm which maps the iris’ distinct patterns and
characteristics. An algorithm is a series of directives that
tell a biometric system how to interpret a specific problem.
Algorithms have a number of steps and are used by the
biometric system to determine if a biometric sample and record
is a match.

Iris’ are composed before birth and, except in the event of
an injury to the eyeball, remain unchanged throughout an
individual’s lifetime. Iris patterns are extremely complex,
carry an astonishing amount of information and have over 200
unique spots. The fact that an individual’s right and left
eyes are different and that patterns are easy to capture,
establishes iris-scan technology as one of the biometrics that
is very resistant to false matching and fraud.

The false acceptance rate for iris recognition systems is 1
in 1.2 million, statistically better than the average
fingerprint recognition system. The real benefit is in the
false-rejection rate, a measure of authenticated users who are
rejected. Fingerprint scanners have a 3 percent
false-rejection rate, whereas iris scanning systems boast
ratees at the 0 percent level.

Iris-scan technology has been piloted in ATM environments
in England, the US, Japan and Germany since as early as 1997.
In these pilots the customer’s iris data became the
verification tool for access to the bank account, thereby
eliminating the need for the customer to enter a PIN number or
password. When the customer presented their eyeball to the ATM
machine and the identity verification was positive, access was
allowed to the bank account. These applications were very
successful and eliminated the concern over forgotten or stolen
passwords and received tremendously high customer approval
ratings.

Airports have begun to use iris-scanning for such diverse
functions as employee identification/verification for movement
through secure areas and allowing registered frequent airline
passengers a system that enables fast and easy identity
verification in order to expedite their path through passport
control.

Other applications include monitoring prison transfers and
releases, as well as projects designed to authenticate on-line
purchasing, on-line banking, on-line voting and on-line stock
trading to name just a few. Iris-scan offers a high level of
user security, privacy and general peace of mind for the
consumer.

A highly accurate technology such as iris-scan has vast
appeal because the inherent argument for any biometric is, of
course, increased security

Benefits of Using Iris Technology

The iris is a thin membrane on the interior of the
eyeball. Iris patterns are extremely complex.
Patterns are individual (even in fraternal or identical
twins).
Patterns are formed by six months after birth, stable
after a year. They remain the same for life.
Imitation is almost impossible.
Patterns are easy to capture and encode

face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular
color=#000000 size=3>Technology Comparison

face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>Method	face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>Coded Pattern	face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>Misidentification rate	face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>Security	face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>Applications
face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>Iris Recognition	face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>Iris pattern	face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>1/1,200,000	face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>High	face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>High-security facilities
face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>Fingerprinting	face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>Fingerprints	face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>1/1,000	face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>Medium	face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>Universal
face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>Hand Shape	face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>Size, length and thickness of hands	face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>1/700	face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>Low	face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>Low-security facilities
face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>Facial Recognition	face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>Outline, shape and distribution of eyes and nose	face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>1/100	face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>Low	face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>Low-security facilities
face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>Signature	face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>Shape of letters, writing order, pen pressure	face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>1/100	face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>Low	face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>Low-security facilities
face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>Voiceprinting	face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>Voice characteristics	face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>1/30	face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>Low	face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular color=#000000 size=2>Telephone service

face=Verdana,Arial,Helvetica,Geneva,Swiss,SunSans-Regular
color=#000000 size=3>Source: AIM Japan, Automatic
Identification Seminar, Sept.14,
2001

Iris-Scan: How it Works

Dr. John Daugman's work in iris recognition form
the basis of this information. Information and images found on his website,
http://www.cl.cam.ac.uk/users/jgd1000, are presented below.

Iris recognition leverages the unique features of
the human iris to perform identification and, in certain cases, verification.

The Iris

Iris recognition is based on visible (via regular
and/or infrared light) qualities of the iris. A primary visible characteristic
is the trabecular meshwork (permanently formed by the 8th month of gestation), a
tissue which gives the appearance of dividing the iris in a radial fashion.
Other visible characteristics include rings, furrows, freckles, and the corona,
to cite only the more familiar.

IrisCode^TM

Expressed simply, iris recognition technology
converts these visible characteristics as a phase sequence into a 512 byte
IrisCode(tm), a template stored for future identification attempts. From the
iris' 11mm diameter, Dr. Daugman's algorithms provide 3.4 bits of data per
square mm. This density of information is such that each iris can be said to
have 266 'degrees of freedom', as opposed to 13-60 for traditional biometric
technologies. This '266' measurement is cited in most iris recognition
literature; after allowing for the algorithm's correlative functions and for
characteristics inherent to most human eyes, Dr. Daugman concludes that 173
"independent binary degrees-of-freedom" can be extracted from his algorithm - an
exceptionally large number for a biometric. A key differentiator of iris-scan
technology is the fact that 512 byte templates are generated for every iris,
which facilitates match speed (capable of matching over 500,000 templates per
second)

Iris Acquisition

The first step is location of the iris by a
dedicated camera no more than 3 feet from the eye. After the camera situates the
eye, the algorithm narrows in from the right and left of the iris to locate its
outer edge. This horizontal approach accounts for obstruction caused by the
eyelids. It simultaneously locates the inner edge of the iris (at the pupil),
excluding the lower 90° because of inherent moisture and lighting issues.

Iris-Scan Issues

Iris-scan technology requires reasonably controlled
and cooperative user interaction - the enrollee must hold still in a certain
spot, even if only momentarily. Many users struggle to interact with the system
until they become accustomed to its operations. In applications whose user
interaction is frequent (e.g. employee physical access), the technology grows
easier to use; however, applications in which user interaction is infrequent
(e.g. national ID) may encounter ease-of-use issues. Over time, with improved
acquisition devices, this issue should grow less problematic.

The accuracy claims associated with iris-scan
technology may overstate the real-world efficacy of the technology. Because the
claimed equal error rates are derived from assessment and matching of ideal iris
images (unlike those acquired in the field), actual results may not live up to
the astronomical projections provided by leading suppliers of the technology.

Lastly, since iris technology is designed to be an
identification technology, fallback procedures may not be as fully developed as
in a verification deployment (users accustomed to identification may not carry
necessary ID, for example). Though these issues do not reduce the effectiveness
of iris recognition technology, they must be kept in mind should a company
decide to implement on iris-based solution.

Iris-Scan Applications

Iris-scan technology has traditionally been
deployed in high-security employee-facing physical access implementations,
although 2002 saw a number of novel, high-profile iris-scan deployments in new
applications. Iridian - the technology’s primary developer - is dedicated to
moving the technology to the desktop, and has had some success in small-scale
logical access deployments. The most prominent recent deployments of iris-scan
technology have been passenger authentication programs at airports in the U.S.,
U.K., Amsterdam, and Iceland; the technology is also used in corrections
applications in the U.S. to identify inmates. A number of developing countries
are considering iris-scan technology for national ID and other large-scale 1:N
applications, although to date it is still believed that the largest deployed
Iridian database spans under 100,000 enrollees. Notable iris-scan applications
include the following.

Project Description	Location	Vertical Sector	Horizontal Application	Application Description	Additional Description
Iris in Pakistan	Pakistan	Government	Civil ID	Tracking	Afghan refugees receive assistance package on first enrollment through UNHCR
Iris Pilot - Logan	US-MA	Travel and Transportation	Phys Acc/T&A	Physical Access	Iris piloted for employee access to security office (LG3000)
JFK Iris Pilot	US-NY	Travel and Transportation	Phys Acc/T&A	Physical Access	1 door to tarmac protected
City Hospital of Bad Reichenhall in Bavaria	Germany	Health care	Phys Acc/T&A	Physical Access	Access control to infant center to prevent kidnappings
Singapore Border Crossing	Singapore	Government	Travel and Transportation	Physical Access	50k day workers enter Singapore from Malaysia daily by motorcycle. Iris-scan does 1:N
UK Passport Office Iris Pilot	UK	Government	Civil ID	Passport	Opt-in pilot to test iris acceptance, part of 6-mo public comment period
Venerable Bede (UK) School - Iris	UK	Education	Retail/ATM/POS	POS	900-pupil school to use Iridian for library check-out and cafeteria payment

Iris-Scan Market Size

Though it is one of the later emerging technologies
in the biometric market, iris-scan is set to grow substantially through 2007.
Iris-scan offers low false match rates and hands-free operation, and is the only
viable alternative to fingerprint technologies in 1:N applications where a
single record must be located. Iris-scan's resistance to false matches is offset
somewhat by the the level of training required to use the system effectively. As
such, iris-scan will primarily be used in applications that require high levels
of security, although convenience-driven deployments (e.g. Privium) will
continue. Iris-scan revenues are projected to grow from $16.2m in 2002 to
$210.2m in 2007. Iris-scan revenues are expected to comprise approximately 5% of
the entire biometric market.

Biometrics Introduction : Biometrics India

2005-05-26T00:00:00.000-07:00

Biometrics

Introduction

Biometrics are automated methods of recognizing a person
based on a physiological or behavioral characteristic. Among the features
measured are; face, fingerprints, hand geometry, handwriting, iris, retinal,
vein, and voice. Biometric technologies are becoming the foundation of an
extensive array of highly secure identification and personal verification
solutions. As the level of security breaches and transaction fraud increases,
the need for highly secure identification and personal verification technologies
is becoming apparent.

Biometric-based solutions are able to provide for confidential financial
transactions and personal data privacy. The need for biometrics can be found in
federal, state and local governments, in the military, and in commercial
applications. Enterprise-wide network security infrastructures, government IDs,
secure electronic banking, investing and other financial transactions, retail
sales, law enforcement, and health and social services are already benefiting
from these technologies.

Biometric-based authentication applications include workstation, network, and
domain access, single sign-on, application logon, data protection, remote access
to resources, transaction security and Web security. Trust in these electronic
transactions is essential to the healthy growth of the global economy. Utilized
alone or integrated with other technologies such as smart cards, encryption keys
and digital signatures, biometrics are set to pervade nearly all aspects of the
economy and our daily lives. Utilizing biometrics for personal authentication is
becoming convenient and considerably more accurate than current methods (such as
the utilization of passwords or PINs). This is because biometrics links the
event to a particular individual (a password or token may be used by someone
other than the authorized user), is convenient (nothing to carry or remember),
accurate (it provides for positive authentication), can provide an audit trail
and is becoming socially acceptable and inexpensive.

Physiological or Behavioral

To elaborate on this definition, physiological biometrics are based on
measurements and data derived from direct measurement of a part of the human
body. Fingerprint, iris-scan, retina-scan, hand geometry, and facial recognition
are leading physiological biometrics.

Behavioral characteristics are based on an action taken by a person. Behavioral
biometrics, in turn, are based on measurements and data derived from an action,
and indirectly measure characteristics of the human body. Voice recognition,
keystroke-scan, and signature-scan are leading behavioral biometric
technologies. One of the defining characteristics of a behavioral biometric is
the incorporation of time as a metric – the measured behavior has a beginning,
middle and end.

Verification vs Identification

A biometric system is essentially a pattern recognition system which makes a
personal identification by determining the authenticity of a specific
physiological or behavioral characteristic possessed by the user. An important
issue in designing a practical system is to determine how an individual is
identified. Depending on the context, a biometric system can be either a
verification (authentication) system or an identification system.

There are two different ways to resolve a person's identity: verification and
identification. Verification (Am I whom I claim I am?) involves confirming or
denying a person's claimed identity. In identification, one has to establish a
person's identity (Who am I?). Each one of these approaches has it's own
complexities and could probably be solved best by a certain biometric system.

In day-to-day life most people with whom you do
business verify your identity. You claim to be someone (your claimed identity)
and then provide proof to back up your claim. For encounters with friends and
family, there is no need to claim an identity. Instead, those familiar to you
identify you, determining your identity upon seeing your face or hearing your
voice.

These two examples illustrate the difference
between the two primary uses of biometrics: identification and verification.

Identification (1:N, one-to-many, recognition)
– The process of determining a person’s identity by performing matches against
multiple biometric templates. Identification systems are designed to determine
identity based solely on biometric information. There are two types of
identification systems: positive identification and negative identification.
Positive identification systems are designed to find a match for a user’s
biometric information in a database of biometric information.

Positive identification answers the “Who am I?,”
although the response is not necessarily a name – it could be an employee ID or
another unique identifier. A typical positive identification system would be a
prison release program where users do not enter an ID number or use a card, but
simply look at a iris capture device and are identified from an inmate database.
Negative identification systems search databases in the same fashion, comparing
one template against many, but are designed to ensure that a person is not
present in a database. This prevents people from enrolling twice in a system,
and is often used in large-scale public benefits programs in which users enroll
multiple times to gain benefits under different names.

Not all identification systems are based on
determining a username or ID. Some systems are designed determine if a user is a
member of a particular category. For instance, an airport may have a database of
known terrorists with no knowledge of their actual identities. In this case the
system would return a match, but no knowledge of the person’s identity is
involved.

Verification (1:1, matching, authentication)
– The process of establishing the validity of a claimed identity by comparing a
verification template to an enrollment template. Verification requires that an
identity be claimed, after which the individual’s enrollment template is located
and compared with the verification template. Verification answers the question,
“Am I who I claim to be?” Some verification systems perform very limited
searches against multiple enrollee records. For example, a user with three
enrolled fingerprint templates may be able to place any of the three fingers to
verify, and the system performs 1:1 matches against the user’s enrolled
templates until a match is found. One-to-few. There is a middle ground between
identification and verification referred to as one-to-few (1:few). This type of
application involves identification of a user from a very small database of
enrollees. While there is no exact number that differentiates a 1:N from a 1:few
system, any system involving a search of more than 500 records is likely to be
classified as 1:N. A typical use of a 1:few system would be access control to
sensitive rooms at a 50-employee company, where users place their finger on a
device and are located from a small database.

Applications

Biometrics is a rapidly evolving technology which is being widely used in
forensics such as criminal identification and prison security, and has the
potential to be used in a large range of civilian application areas. Biometrics
can be used to prevent unauthorized access to ATMs, cellular phones, smart
cards, desktop PCs, workstations, and computer networks. It can be used during
transactions conducted via telephone and internet (electronic commerce and
electronic banking). In automobiles, biometrics can replace keys with key-less
entry devices.

Biometrics technologies

The primary biometric disciplines
include the following:

Fingerprint (optical, silicon,
ultrasound, touch less)

Facial recognition (optical and
thermal)

Voice recognition (not to be
confused with speech recognition)

Iris-scan

Retina-scan

Hand geometry

Signature-scan

Keystroke-scan

Palm-scan (forensic use only)

Disciplines with reduced
commercial viability or in exploratory stages include:

Ear shape

Odor (human scent)

Vein-scan (in back of hand or
beneath palm)

Finger geometry (shape and
structure of finger or fingers)

Nailbed identification (ridges
in fingernails)

Gait recognition (manner of walking)

How it works

Biometric systems convert data derived from
behavioral or physiological characteristics into templates, which are used for
subsequent matching. This is a multi-stage process whose stages are described
below.

Enrollment - The process whereby a user’s
initial biometric sample or samples are collected, assessed, processed, and
stored for ongoing use in a biometric system. Enrollment takes place in both 1:1
and 1:N systems. If users are experiencing problems with a biometric system,
they may need to re-enroll to gather higher quality data.

Submission - The process
whereby a user provides behavioral or physiological data in the form of
biometric samples to a biometric system. A submission may require looking in the
direction of a camera or placing a finger on a platen. Depending on the
biometric system, a user may have to remove eyeglasses, remain still for a
number of seconds, or recite a pass phrase in order to provide a biometric
sample.

Acquisition device – The
hardware used to acquire biometric samples. The following acquisition devices
are associated with each biometric technology:

Technology	Acquisition Device
Fingerprint	Desktop peripheral, PCMCIA card, mouse, chip or reader embedded in keyboard
Voice recognition	Microphone, telephone
Facial recognition	Video camera, PC camera, single-image camera
Iris-scan	Infrared-enabled video camera, PC camera
Retina-scan	Proprietary desktop or wall-mountable unit
Hand geometry	Proprietary wall-mounted unit
Signature-scan	Signature tablet, motion-sensitive stylus
Keystroke-scan	Keyboard or keypad

Biometric sample - The
identifiable, unprocessed image or recording of a physiological or behavioral
characteristic, acquired during submission, used to generate biometric
templates. Also referred to as biometric data. The following sample types are
associated with each biometric technology:

Technology	Biometric Sample
Fingerprint	Fingerprint image
Voice recognition	Voice recording
Facial recognition	Facial Image
Iris-scan	Iris Image
Retina-scan	Retina Image
Hand geometry	3-D image of top and sides of hand and fingers
Signature-scan	Image of signature and record of related dynamics measurements
Keystroke-scan	Recording of characters typed and record of related dynamics measurements

Feature extraction - The
automated process of locating and encoding distinctive characteristics from a
biometric sample in order to generate a template. The feature extraction process
may include various degrees of image or sample processing in order to locate a
sufficient amount of accurate data. For example, voice recognition technologies
can filter out certain frequencies and patterns, and fingerprint technologies
can thin the ridges present in a fingerprint image to the width of a single
pixel. Furthermore, if the sample provided is inadequate to perform feature
extraction, the biometric system will generally instruct the user to provide
another sample, often with some type of advice or feedback.

The manner in which biometric
systems extract features is a closely guarded secret, and varies from vendor to
vendor. Common physiological and behavioral characteristics used in feature
extraction include the following:

Technology	Feature Extracted
Fingerprint	Location and direction of ridge endings and bifurcations on fingerprint
Voice recognition	Frequency, cadence and duration of vocal pattern
Facial recognition	Relative position and shape of nose, position of cheekbones
Iris-scan	Furrows and striations in iris
Retina-scan	Blood vessel patterns on retina
Hand-scan	Height and width of bones and joints in hands and fingers
Signature-scan	Speed, stroke order, pressure, and appearance of signature
Keystroke-scan	Keyed sequence, duration between characters

Template – A comparatively
small but highly distinctive file derived from the features of a user’s
biometric sample or samples, used to perform biometric matches. A template is
created after a biometric algorithm locates features in a biometric sample. The
concept of the template is one of biometric technology’s defining elements,
although not all biometric systems use templates to perform biometric matching:
some voice recognition system utilize the original sample to perform a
comparison.

Depending on when they are
generated, templates can be referred to as enrollment templates or verification
templates. Enrollment templates are created upon the user’s initial interaction
with a biometric system, and are stored for usage in future biometric
comparisons. Verification templates are generated during subsequent verification
attempts, compared to the stored template, and generally discarded after the
comparison. Multiple samples may be used to generate an enrollment template –
facial recognition, for example, will utilize several facial images to generate
an enrollment template. Verification templates are normally derived from a
single sample – a template derived from a single facial image can be compared to
the enrollment template to determine the degree of similarity.

Just as the feature extraction
process is a closely held secret, the manner in which information is organized
and stored in the template is proprietary to biometric vendors. Biometric
templates are not interoperable – a template generated in vendor A’s fingerprint
system cannot be compared to a template generated in vendor B’s fingerprint
system.

Biometric decision-making is frequently misunderstood.
For the vast majority of technologies and systems, there is no such thing as a
100% match, though systems can provide a very high degree of certainty. The
biometric decision-making process is comprised of various components, as
indicated below.

Matching - The comparison of biometric templates to determine their degree of
similarity or correlation. A match attempt results in a score that, in most
systems, is compared against a threshold. If the score exceeds the threshold,
the result is a match; if the score falls below the threshold, the result is a
non-match.

Biometric comparisons take place when proprietary algorithms process biometric
templates. These algorithms manipulate the data contained in the template in
order to make valid comparisons, accounting for variations in placement,
background noise, etc. Without the vendor algorithm, there is no way to compare
biometric templates – comparing the bits which comprise the templates does not
indicate if they came from the same user. The bits must be processed by the
vendor as a precondition of comparison.

The matching process involves the comparison of the match template, created upon
sample submission, with the reference template(s) already on file. In 1:1
verification systems, there is generally a single match template matched against
a reference template. In 1:N identification systems, the single match template
can be matched against dozens, thousands, even millions of reference templates.

In most systems, reference and match templates should never be identical. An
identical match is an indicator that some sort of fraud is taking place, such as
the resubmission of an intercepted or otherwise compromised template.

Score – A number indicating the degree of similarity or correlation of a
biometric match. Traditional verification methods – passwords, PINs, keys, and
tokens - are binary, offering only a strict yes/no response. This is not the
case with most biometric systems. Nearly all biometric systems are based on
matching algorithms that generate a score subsequent to a match attempt. This
score represents the degree of correlation between the match template and the
reference template. There is no standard scale used for biometric scoring: for
some vendors a scale of 1-100 might be used, others might use a scale of –1 to
1; some vendors may use a logarithmic scale and others a linear scale.
Regardless of the scale employed, this verification score is compared to the
system’s threshold to determine how successful a verification attempt has been.

Incidentally, many systems return a score during enrollment, referred to as an
enrollment score or quality score. This score refers to how successful the
extraction process was at finding distinctive features in the biometric sample.
If the sample was rich in information, there will likely be a high enrollment
score. This score is not used in the matching process, but might be used to
determine whether a user can enroll successfully. A low quality score may
indicate that the user cannot be reliable verified.

Threshold - A predefined number, often controlled by a biometric system
administrator, which establishes the degree of correlation necessary for a
comparison to be deemed a match. If the score resulting from template comparison
exceeds the threshold, the templates are a “match” (though the templates
themselves are not identical).

When a biometric system is set to low security, the threshold for a successful
match is more forgiving than when a system is set to high security.

Decision – The result of the comparison between the score and the threshold. The
decisions a biometric system can make include match, non-match, and
inconclusive, although varying degrees of strong matches and non-matches are
possible. Depending on the type of biometric system deployed, a match might
grant access to resources, a non-match might limit access to resources, while
inconclusive may prompt the user to provide another sample.

One of the most interesting facts about most biometric technologies is that
unique biometric templates are generated every time a user interacts with a
biometric system. As an example, two immediately successive placements of a
finger on a biometric device generate entirely different templates. These
templates, when processed by a vendor’s algorithm, are recognizable as being
from the same person, but are not identical. In theory, a user could place the
same finger on a biometric device for years and never generate an identical
template.

Therefore, for most technologies, there is simply no such thing as a 100% match.
This is not to imply that the systems are not secure – biometric systems may be
able to verify identify with error rates of less than 1/100,000 or 1/1,000,000.
However, claims of 100% accuracy are misleading and are not reflective of the
technology’s basic operation.

Benefits

For employers

Reduced costs – password
maintenance

Reduced costs – no buddy punching

Increased security – no shared or compromised passwords

Increased security – deter and detect fraudulent account access

Increased security – no badge sharing in secure areas

Competitive advantage – familiarity with advanced technology

For employees

Convenience – no passwords to
remember or reset

Convenience – faster login

Security – confidential files can be stored securely

Non-repudiation – biometrically transactions difficult to refute

For consumers

Convenience – no passwords to
remember or reset

Security – personal files, including emails, can be secured

Security – online purchases safer when enabled by biometric

Privacy – ability to transact anonymously

For retailers (online and
point-of-sale)

Reduced costs – biometric users
less likely to commit fraud

Competitive advantage – first to offer secure transaction method

Security – account access much more secure than via password

For public sector usage

Reduced costs – strongest way to
detect and deter benefits fraud

Increased trust – reduced entitlement abuse

Are Biometric Systems Difficult to Use?

Biometrics are much easier to use than one might
expect. Here is a brief technology-by-technology summary of how one interacts
with biometric systems.

Fingerprint. When prompted, the user gently
places his or her finger on a postage-stamp sized optical or silicon surface.
This surface, known as a platen, is built into a peripheral device, mouse,
keyboard, or PCMCIA card. The user generally must hold the finger in place for
1-2 seconds, during which automated comparison and matching takes place. After a
successful match, the user has access to programs, files, or resources. Typical
verification time from “system ready” prompt: 2-3 seconds.

Facial recognition. User faces the camera,
preferably positioned within 24 inches of the face. Generally, the system will
locate one’s face very quickly and perform matches against the claimed identity.
In some situations, the user may need to alter his facial aspect slightly to be
verified. Typical verification time from “system ready” prompt: 3-4 seconds.

Voice recognition. User positions him or
herself near the acquisition device (microphone, telephone). At the prompt, user
either recites enrollment pass phrase or repeats pass phrase given by the
system.

Typical verification time from “system ready” prompt: 4-6 seconds.

Iris-scan. User positions him or herself
near the acquisition device (peripheral or standalone camera). User centers eye
on device so he or she can see the eye’s reflection. Depending on the device,
the user is between 2-18 inches away. Capture and verification are nearly
immediate. Typical verification time from “system ready” prompt: 3-5 seconds.

Retina-scan. User looks
into a small opening on a desktop or wall-mounted device. User holds head very
still, looking at a small green light located within the device. Typical
verification time from “system ready” prompt: 10-12 seconds.

Hand geometry. User places
hand, palm-down, on an 8 x 10 metal surface with five guidance pegs. Pegs ensure
that fingers are placed properly, ensure correct hand position. Typical
verification time from “system ready” prompt: 2-3 seconds.

Signature-scan. User
positions himself to sign on tablet (if applicable). When prompted, user signs
name in tablet’s capture area. Typical verification time from “system ready”
prompt: 4-6 seconds.

Keystroke-scan. User types his or her
password or pass phrase. Typical verification time from “system ready” prompt:
2-3 seconds.

Security of biometrics template

If my template is compromised, does that mean that
I can never use the biometric again?

Not in a well-designed system. If a criminal steals
or guesses your password, it is very easy to have it changed. There is a fear,
however, that if a criminal gets hold of a biometric template, the damage is
irreparable - there is no way to change that part of your body. Although
templates are often encrypted when in transit and storage in order to protect
against such an occurrence, what happens if a template is compromised?

The answer depends on how well a
biometric system is designed. If a system allows a template to be inserted into
the verification process without ensuring that this template came from an actual
placement, a compromised template can pose a problem. However, a well-designed
system will ensure that the information it is analyzing is not a recording but
is in fact a new sample.

One way to assure that a new
template is being submitted is to seed the request for a sample. This involves
the biometric system sending an encrypted random number (known as a seed) to the
biometric sensor. This number can be encrypted such that only the sensor itself
can decrypt the message. When returning the biometric template, the sensor also
sends the seed number back (encrypted). This ensures that the template being
sent was created immediately after the request for the template (as opposed to
an old template that has been recorded and played back).

The size of a template varies by
technology and vendor. It varies from 9 bytes to as much a 2kb. Fingerprint
template is normally about 500Bytes in size. Templates can be stored in
databases or files.

What Factors Cause Biometric Systems to Fail?

Biometric system performance
varies according to sample quality and the environment in which the sample is
being submitted. While it is not possible to definitely state if a biometric
submission will be successful, it is possible to locate factors that can reduce
affect system performance.

The IBG Strike System details,
technology-by-technology, aspects that work against a successful verification.
Some of these strikes are listed below.

Fingerprint

Cold finger

Dry/oily finger

High or low humidity

Angle of placement

Pressure of placement

Location of finger on platen
(poorly placed core)

Cuts to fingerprint

Manual activity that would mar
or affect fingerprints (construction, gardening)

Voice recognition

Cold or illness that affects
voice

Different enrollment and
verification capture devices

Different enrollment and
verification environments (inside vs. outside)

Speaking softly

Variation in background noise

Poor placement of microphone /
capture device

Quality of capture device

Facial recognition

Change in facial hair

Change in hairstyle

Lighting conditions

Adding/removing hat

Adding/removing glasses

Change in weight

Change in facial aspect (angle
at which facial image is captured)

Too much or too little
movement

Quality of capture device

Change between enrollment and
verification cameras (quality and placement)

‘Loud’ clothing that can
distract face location

Iris-scan

Too much movement of head or
eye

Glasses

Colored contacts

Retina-scan

Too much movement of head or
eye

Glasses

Hand geometry

Jewelry

Change in weight

Bandages

Swelling of joints

Signature-scan

Signing too quickly

Different signing positions
(e.g., sitting vs. standing)

In addition, for many systems, an
additional strike occurs when a long period of time has elapsed since enrollment
or since one’s last verification. If significant time has elapsed since
enrollment, physiological changes can complicate verification. If time has
elapsed since a user’s last verification, the user may have “forgotten” how he
or she enrolled, and may place a finger differently or recite a pass phrase with
different intonation. For the most part, a single strike will probably not
materially affect the performance of a given system. However, as you have more
and more strikes for a given submission, your chances of a successful
verification diminish.

These strikes do not include
inherent characteristics such as age, ethnicity, or gender, which can also
affect system accuracy. The performance of many biometric systems varies for
specific populations.

Application design

As in all good application designs, it is the business process requirements
which should drive the design - not the other way around. Similarly the specific
type of biometric chosen, i.e., fingerprints, iris codes, hand geometry etc.
should reflect the application requirements - the application should not be a
slave to an individual biometric methodology.

A successful application development and deployment scenario may follow a
path something like the following;

Identify the business and operational requirements
clearly, together with any current problems and the effect they are having on
the situation.

Develop and agree a suitable business process which has
the potential to significantly improve on the current situation, given the
current state of technology.

Quantify the operational logistics such as (in an
access control context) number of people, time profile / distribution of
transactions, type of entry point, target transaction time, environmental
considerations, availability and profile of system operators and so on.

Analyse existing situation and processes in order to
identify legacy requirements and system interaction - it may be necessary to
retain or assure compatibility with certain existing processes.

Design a system architecture which accounts for all of
the above whilst remaining open for future development and enhancement.

Design an operating methodology and user interface
which satisfies the above requirements in an intuitive and attractive manner.

Choose the appropriate front end technology accordingly
(i.e., biometric / biometric and chip card etc.) ensuring that the biometric
methodology is the most suitable for this application.

Interface the biometric / token technology with your
system.

Thoroughly test and document the system in house before
demonstrating the system to the client and agreeing and documenting any design
changes.

Develop and schedule an operator training programme
together with the provision of system manuals as necessary.

Install and commission the system having surveyed the
site and noted relevant conditions and with due consideration to existing
systems.

Hand over the system after ensuring that operators have
a comprehensive understanding of the functionality and that all operating data
is present and correct.

In the above example, you will notice that the final choice of a biometric
came relatively far down the list. We should only be considering this parameter
once we have fully understood the business requirement and the potential benefit
that adopting a biometric system might bring.

In defining the specification required, we should concern ourselves with
perceived ease of use, acceptable transaction time, contingency measures for
errors, where the biometric template should be stored, enrolment procedures and
logistics and general compatibility and connectivity issues.

We should also understand the distinction between verification and
identification. In short, verification is a straightforward one to one check
whereby we are comparing a live biometric sample with a single stored template
with a simple match or no match result. Identification is a different kettle of
fish entirely as we may be seeking to compare a live biometric sample with
hundreds, thousands or conceivably even millions of stored templates. The
probability of errors multiplies with the number of templates in the database.
Currently, there is really only one commercially available product which offers
the promise of practical identification from a large database of templates. For
the majority of applications we are probably going to be concerned with
biometric verification..

We must also consider where the biometric template (the individual reference
derived from taking a biometric sample or series of samples) will be stored. It
may be that the template is stored on a token such as a chip card and input into
the system by the user prior to verification. This would certainly allow for a
large user base as well as a degree of portability between systems and would
provide for automatic updating of templates if appropriate. Alternatively, we
may decide to keep the templates on a central database and call them from either
a card swipe or PIN input for comparison. This decision will naturally have an
impact on system hardware and configuration - if we are maintaining a central
database we had better be sure about our system host and it's communication with
the biometric readers, not to mention the usual database maintenance and backup
requirements.

Whilst we are on the subject of hardware, it is worth stressing the
importance of understanding the cabling and line termination requirements of
different communication protocols. Lack of attention to detail in this area can
often result in temperamental performance and perceived intermittent faults
which can be difficult to trace subsequently. Whilst this may seem like stating
the obvious, it is surprising how often otherwise well designed systems are
tripped over by poor installation practice.

You will have noticed that we have got a long way into this paper without
trundling out the usual marketing promises about biometrics or contemplating the
old chestnuts of false accepts / false rejects etc. This is deliberate - one can
concentrate too much on the theoretical individual device performance issues.
The performance we should concern ourselves with is that of the entire system,
not individual components. In the real world, theoretical performance may be
influenced greatly by other less quantitative parameters. For example, a badly
sited reader which is difficult for individuals to use comfortably will almost
certainly result in increased false rejects, even though the system may be
functioning properly. Similarly, a lack of training or understanding among both
system administrators and regular users will play havoc with your anticipated
performance. The operational processes coupled to the perception and attitude of
the user are as much of a performance criterion as biometric hardware
specifications. These elements, coupled with overall system design and component
performance combine to produce the Total System Performance (TSP). It is the TSP
that we should have uppermost in our minds throughout the development and
implementation of the entire project.

To put this into perspective, it would seem rather pointless to have lengthy
discussions about the inclined valve angle on a one litre petrol engine which we
are fitting into a three ton vehicle - we should be asking about power to weight
ratios and what sort of engine we need to propel this vehicle at the required
speed. The same is true of our biometric system. We must consider the system as
a whole, together with our business related objectives for implementing such a
system.

So far, we have discussed some of the issues inherent in a typical systems
supplier / client situation. In certain cases, the end user (or retained systems
house) may wish to buy in the component technology on an OEM basis and develop
their own custom application according to precise requirements. In the early
days of biometrics this would have been quite difficult with many of the
proprietary products available. These days life is a lot easier for the
application development team as several of the leading device manufacturers have
taken the trouble to make available a Software Development Kit (SDK) for use
with their product. This usually takes the form of a set of DLL's which the
developer may call from his application in order to access various functions of
the device. This allows the developer to concentrate on the user interface and
program logic without having to get too involved with the low level coding
detail.

This is certainly a step forward and is to be welcomed. However, it is a
little device specific in the sense that if you decided later on to use a
different front end biometric device, then you would need to rewrite your
application accordingly. This may be acceptable in some instances, but what if
you wish to use more than one type of biometric device on your system? This is
not unreasonable. You may wish to use a dual biometric for high security
reasons, or to use different biometrics in different areas for environmental
reasons. This can complicate matters somewhat. It would be nice perhaps if there
were a universally accepted biometric Application Programming Interface (API)
which developers could use in order to mix biometric methodologies within a
single system. In fact, there has been much work undertaken in this context and
by the time you read this paper at least one such API should be freely
available. The question is, will the biometric manufacturers be happy to comply
with and support such an initiative? I hope that they will, but suspect that
this may take a while to become embedded in biometric culture.

What of the future? The is no doubt that biometric technology is mature and
eminently useable across a wide variety of advanced personal ID related
applications. Both the systems integrator and the end user have a wider choice
than ever of front end biometric components and it is easier than it has ever
been to integrate these components into bespoke systems. Individual unit cost is
still relatively high for biometric products, but this too is changing and
several manufacturers are introducing lower cost OEM modules to the market
place.

In short, if you have an operational problem that biometrics might solve
there is no reason to sit on the fence any longer - biometrics are alive and
well and available off the shelf at a location near you!