2. VIGNANA BHARATHI INSTITUTE OF TECHNOLOGY
Abstract:
The pressures on today’s system
administrators to have secure systems
are ever increasing. One area where
security can be improved is in
authentication
and
identification.
Biometrics provides a promising
solution meeting all demands. Biometric
identification utilizes physiological and
behavioral characteristics to authenticate
a person’s identity which include facial
recognition, fingerprints, palm prints,
hand geometry, retinal patterns and iris
patterns and signature, voice pattern and
key stroke dynamics. Many have
suffered
from
high
cost
and
unsatisfactory error rates.
The technology is accurate, easy to use,
non-intrusive, and difficult to forge and,
despite what many people may think is
actually quite a fast system once initial
enrollment has taken place With new
technologies the eyes are more than
“windows to your soul.” People are
carrying with them a living key or
password that will never be forgotten
and will always be there. The technology
is available now through work in
computer vision, pattern recognition, and
man-machine interface to create a
reliable lock that a person's iris pattern
will open. The randomness of iris
patterns has a very high dimensionality
making recognition decisions reliable
with a high level of confidence.
This paper, at the outset, throws light on
the technology involved in iris
recognition, the algorithms followed by
a statistical analysis highlighting its edge
over other biometric identification
systems and its applications in the
present day world. This paper is
intended for security practitioners who
are knowledgeable, but not technically
or scientifically oriented.
Introduction:
Iris recognition is a method of biometric
authentication
that
uses
pattern
recognition techniques based on highresolution images of the irides of an
individual's eyes. Not to be confused
with another less prevalent ocular-based
technology,
retina scanning, iris
recognition uses camera technology, and
subtle IR illumination to reduce specular
reflection from the convex cornea to
create images of the detail-rich, intricate
structures of the iris. These unique
structures
converted
into
digital
templates,
provide
mathematical
representations of the iris that yield
unambiguous positive identification of
an individual.
Iris recognition efficacy is rarely
impeded by glasses or contact lenses.
Iris technology has the smallest outlier
(those who cannot use/enroll) group of
all biometric technologies. The only
biometric authentication technology
designed for use in a one-to many search
environment, a key advantage of iris
recognition is its stability, or template
longevity as, barring trauma, a single
enrollment can last a lifetime.
Breakthrough work to create the iris
recognition algorithms required for
image acquisition and one-to-many
3. matching was pioneered by John G.
Daugman, Ph.D, OBE (University of
Cambridge Computer Laboratory), who
holds key patents on the method. These
were utilized to effectively debut
commercialization of the technology in
conjunction with an early version of the
Iris Access system designed and
manufactured
by
Korea's
LG
Electronics. Daugman's algorithms are
the basis of almost all currently (as of
2006) commercially deployed irisrecognition systems. It has a so far
unmatched practical false-accept rate of
zero; that is there is no known pair of
images of two different irises that the
Daughman algorithm in its deployed
configuration mistakenly identifies as
the same. (In tests where the matching
thresholds are – for better comparability
– changed from their default settings to
allow a false-accept rate in the region of
10−3 to 10−4, the Iris Code false-reject
rates are comparable to the most
accurate
single-finger
fingerprint
matchers.).
An Overview of Biometrics:
Biometrics refers to the automatic
identification of a person based on
his/her physiological or behavioral
characteristics.
This
method
of
identification offers several advantages
over traditional methods involving ID
cards (tokens)
or PIN numbers
(passwords) for various reasons: (i) the
person to be identified is required to be
physically present at the point-ofidentification; (ii) identification based on
biometric techniques obviates the need
to remember a password or carry a
token. With the increased integration of
computers and Internet into our
everyday lives, it is necessary to protect
sensitive and personal data. By replacing
PINs (or using biometrics in addition to
PINs), biometric techniques can
potentially prevent unauthorized access
to ATMs, cellular phones, laptops, and
computer networks. Unlike biometric
traits, PINs or passwords may be
forgotten, and tokens like passports and
driver's licenses may be forged, stolen,
or lost. Thus, biometric systems are
being deployed to enhance security and
reduce
financial
fraud.
Various
biometric traits are being used for realtime recognition, the most popular being
face, iris and fingerprint. However, there
are biometric systems that are based on
retinal scan, voice, signature and hand
geometry.
A biometric system is essentially a
pattern recognition system which
recognizes a user by determining the
authenticity of a specific physiological
or behavioral characteristic possessed by
the user. Several important issues must
be considered in designing a practical
biometric system. First, a user must be
enrolled in the system so that his
biometric template can be captured. This
template is securely stored in a central
database or a smart card issued to the
user. The template is retrieved when an
individual needs to be identified.
Depending on the context, a biometric
system can operate either in verification
(authentication) or an identification
mode.
Verification vs. Identification:
There are two different ways to
recognize a person: verification and
identification. Verification involves
confirming or denying a person's
claimed identity. On the other hand, in
identification, the system has to
recognize a person (Who am I ? ) from a
4. list of N users in the template database.
Identification is a more challenging
problem because it involves 1: N
matching compared to 1:1 matching for
verification.
Operating principle
An iris-recognition algorithm first has to identify the approximately concentric circular
outer boundaries of the iris and the pupil in a photo of an eye. The set of pixels covering
only the iris is then transformed into a bit pattern that preserves the information that is
essential for a statistically meaningful comparison between two iris images. The
mathematical methods used resemble those of modern lossy compression algorithms for
photographic images. In the case of Daugman's algorithms, a Gabor wavelet transform is
used in order to extract the spatial frequency range that contains a good best signal-tonoise ratio considering the focus quality of available cameras. The result are a set of
complex numbers that carry local amplitude and phase information for the iris image. In
Daugman's algorithms, all amplitude information is discarded, and the resulting 2048 bits
that represent an iris consist only of the complex sign bits of the Gabor-domain
representation of the iris image. Discarding the amplitude information ensures that the
template remains largely unaffected by changes in illumination and virtually negligibly
by iris color, which contributes significantly to the long-term stability of the biometric
template. To authenticate via identification (one-to many template matching) or
verification (one-to one template matching) a template created by imaging the iris, is
compared to a stored value template in a database. If the Hamming Distance is below the
decision threshold, a positive identification has effectively been made.
A practical problem of iris recognition is that the iris is usually partially covered by eye
lids and eye lashes. In order to reduce the false-reject risk in such cases, additional
algorithms are needed to identify the locations of eye lids and eye lashes, and exclude the
bits in the resulting code from the comparison operation.
5. How Iris works:
Reliable automatic recognition of
persons has long been an attractive
goal. As in all pattern recognition
problems, the key issue is the
relation between interclass and intraclass variability: objects can be
reliably classified only if the
variability among different instances
of a given class is less than the
variability between different classes.
For example in face recognition,
difficulties arise from the fact that
the face is a changeable social organ
displaying a variety of expressions,
as well as being an active 3D object
whose image varies with viewing
angle,
pose,
illumination,
accoutrements, and age. It has been
shown that for facial images taken at
least one year apart; even the best
current algorithms have error rates of
43%. Against this intra-class (same
face)
variability,
inter-class
variability is limited because
different faces possess the same
basic set of features, in the same
canonical geometry.
For all of these reasons, iris patterns
become interesting as an alternative
approach
to
reliable
visual
recognition of persons when imaging
can be done at distances of less than
a meter, and especially when there is
a need to search very large databases
without incurring any false matches
despite a huge number of
possibilities.
6. postnatal years. Its complex pattern can
contain many distinctive features such as
arching ligaments, furrows, ridges,
crypts, rings, corona, freckles, and a
zigzag collarets, some of which may be
seen in Figure2.
Figure 1: Example of an iris pattern,
imaged monochromatically at a distance
of about 35 cm. The outline overlay
shows results of the iris and pupil
localization and eyelid detection steps.
The bit stream in the top left is the result
of demodulation with
complex-valued 2D Gabor wavelets to
encode the phase sequence of the iris
pattern.
In addition, as an internal (yet externally
visible) organ of the eye, the iris is well
protected from the environment and
stable over time. As a planar object its
image is relatively insensitive to angle of
illumination and changes in viewing
angle cause only affine transformations;
even the nonfat net pattern distortion
caused by papillary dilation is readily
reversible. Finally, the ease of localizing
eyes in faces, and the distinctive annular
shape of the iris, Facilitate reliable and
precise isolation of this feature and the
creation
of
a
size-invariant
representation.
The iris begins to form in the third
month of gestation and the structures
creating its pattern are largely complete
by the eighth month, although pigment
accretion can continue into the first
Iris color is determined mainly by the
density of melanin pigment in its
anterior layer and stroma, with blue
irises resulting from an absence of
pigment:
long
wavelength
light
penetrates and is absorbed by the
pigment epithelium, while shorter
wavelengths are reflected and scattered
by the stroma. All testing organizations
have reported a false match rate of 0 in
their tests, some of which involved
millions of iris pairings. This paper
explains how the algorithms work, and
presents new data on the statistical
properties and singularity of iris patterns
based on 9.1 million comparisons.
Finding an Iris in an Image
To capture the rich details of iris
patterns, an imaging system should
resolve a minimum of 70 pixels in iris
radius. In the field trials to date, a
resolved iris radius of 100 to 140 pixels
has been more typical. Monochrome
7. CCD cameras (480 x 640) have been
used because NIR illumination in the
700nm -900nm band was required for
imaging to be invisible to humans. Some
imaging platforms deployed a wide
angle camera for coarse localization of
eyes in faces, to steer the optics of a
narrow-angle pan/tilt camera that
acquired higher resolution images of
eyes. There exist many alternative
methods for finding and tracking Facial
features such as the eyes
Images passing a minimum focus
criterion were then analyzed to find the
iris, with precise localization of its
boundaries using a coarse-to-fine
strategy terminating in single-pixel
precision estimates of the center
Coordinates and radius of both the iris
and the pupil. Although the results of the
iris search greatly constrain the pupil
search, concentricity of these boundaries
cannot be assumed. Very often the pupil
center is nasal, and inferior, to the iris
center. Its radius can range from 0.1 to
0.8 of the iris radius. Thus, all three
parameters defining the pupillary circle
must be estimated separately from those
of the iris. A very effective
integrodifferential
operator
for
determining these parameters is:
Where I(x; y) is an image such as Fig 1
containing an eye. The operator searches
over the image domain (x; y) for the
maximum in the blurred partial
derivative with respect to increasing
radius r, of the normalized contour
integral of I(x; y) along a circular arc
of radius r and center coordinates (x0;
y0). The result of all these localization
operations is the isolation of iris tissue
from other image regions, as illustrated
in Fig 1 by the graphical overlay on the
eye.
Phase-Quadrant
Code
Demodulation
Figure 3: The phase demodulation
process used to encode iris patterns.
Local regions of an iris are projected
(Eqt 2) onto quadrature 2D Gabor
wavelets, generating complex-valued
coefficients whose real and imaginary
parts specify the coordinates of a phasor
in the complex plane. The angle of each
phasor is quantized to one of the four
quadrants, setting two bits of phase
information. This process is repeated all
across the iris with many wavelet sizes,
frequencies, and orientations, to extract
2,048 bits.
8. Altogether 2,048 such phase bits (256
bytes) are computed for each iris, but in
a major improvement over the earlier
(Daugman 1993) algorithms, now an
equal number of masking bits are also
computed to signify whether any iris
region is obscured by eyelids, contains
any eyelash occlusions, specular
reflections, boundary artifacts of hard
contact lenses, or poor signal-to-noise
ratio and thus should be ignored in the
demodulation code as artifact.
Figure 4: Illustration that even for poorly
focused eye images, the bits of a
demodulation phase sequence are still
set, primarily by random CCD noise.
This prevents poorly focused eye images
from resembling each other in the
pattern matching stage, in the way that
(e.g.) poorly resolved face images look
alike and can be confused with each
other. Only phase information is used for
recognizing irises because amplitude
information is not very discriminating,
and it depends upon extraneous factors
such as imaging contrast, illumination,
and camera gain.
The
Test
Independence:
of
Statistical
The key to iris recognition is the failure
of a test of statistical independence,
which involves so many degrees-offreedom that this test is virtually
guaranteed to be passed whenever the
phase codes for two different eyes are
compared, but to be uniquely failed
when any eye's phase code is compared
with another version of itself. The test of
statistical independence is implemented
by the simple Boolean Exclusive-OR
operator (XOR) applied to the 2,048 bit
phase vectors that encode any two iris
patterns, masked (AND'ed) by both of
their corresponding mask bit vectors to
prevent
non-iris
artifacts
from
influencing iris comparisons. The XOR
operator N detects disagreement between
any corresponding pair of bits, while the
AND operator T ensures that the
compared bits are both deemed to have
been uncorrupted by eyelashes, eyelids,
specular reflections, or other noise.
9. Figure 4: Distribution of Hamming
Distances from all 9.1 million possible
comparisons between different pairs of
irises in the database. The histogram
forms a perfect binomial distribution
solid curve. The data implies that it is
extremely improbable for two different
irises to disagree in less than about a
third of their phase information.
Informative searches are performed at a
rate of about 100,000 irises per second.
Network Security: Authenticam
with PrivateID
•
•
•
Iris Scanning
Services:
Products
and
The iris recognition camera can
be integrated into a variety of
software applications to provide
security for information and
electronic commerce.
Authenticam incorporates highresolution
videoconference
ability. It is estimated that the
return on investment (ROI) can
be realized in less than a year
making this highly affordable.
This innovation will allow
companies to control access to
computer
workstations,
networks, and sensitive corporate
data, as well as positively
identifying system users.
Next-generation
IrisPass:
•
•
ATMs:
Built by Japan’s OKI Electric
Industry, IrisPass is currently
integrating iris scanning in ATM
machines in Asia and the US.
IrisPass will eliminate the need
for PINs to make identification
of account ownership.
This technology may also be
used for in-bank teller stations
for account verification.
Issues to Consider
•
Other biometrics recognition
systems include fingerprinting,
palm prints, hand geometry, nail
bed
identification,
facial
recognition, and retinal scan.
10. •
•
•
Behavioral characteristics used
for identification are signature
dynamics, keyboard dynamics,
and voice recognition.
Iris and retina scans are the most
accurate
of all
biometric
techniques and, currently, the
most costly.
The entire biometrics market is
projected to reach $10 billion by
2008.
Speed:
•
•
Iris recognition systems can
cycle through 1,500,000 matches
per minute, which is 20 times
greater processing speed than any
other biometrics systems.
In real-life applications this
translates into an identity
decision being made in seconds.
The enrollment process is also
speedily accomplished, typically
in three minutes or less.
•
•
•
•
Costs:
•
Safety and Perceived Invasiveness:
•
•
•
Enrollment and use of an iris
recognition system requires no
contact, only cooperation of the
user.
The devices capture images of
the eye from a comfortable
distance without bright lights or
lasers.
The Iris Code is hashed and
encrypted as a security measure
to prevent theft. If a person feels
their recognition patterns have
been
compromised,
reenrollment is possible an infinite
number of times by using a
permuted Iris Code.
Iris recognition because it looks
at the exterior part of the eye,
unlike retinal scans that look at
vascular patterns inside the eye,
is not invasive.
Also, there is no possibility of
gathering information such as
medical conditions, a possibility
with retinal scans.
Both irises and retinas are stable
throughout a lifetime, except in
the case of degenerative diseases
that may affect the retina.
There is no need to remove
glasses or contact lenses during
identification. As long as they do
not obscure the iris, recognition
can be made through them. Iris
recognition can be hampered by
partially occluded or drooping
eyelids.
•
•
•
•
Iris scanning can increase
profitability by minimizing both
costs
and
vulnerabilities
associated with password and
password management.
Research indicates US businesses
spend an average of $200 per
person a year on password
management.
The value proposition of
implementing an iris recognition
system is three-dimensional.
Cost, accuracy, and ease of use
are all important considerations.
Recent advances in camera
technology is bringing down the
cost of iris recognition. Camera
prices have gone down while
processing ability has gone up.
The size of the camera has
decreased also. With a strategic
11. •
partnership with Panasonic, the
costs
have
gone
down
significantly.
Both iris scanning and retina
scanning are at the upper end of
the scale in cost compared to
other biometric systems.
Ease of Use:
•
•
•
•
•
Many of the users who have
already encountered iris-scanning
technology
consider
it
a
convenience rather than an
intrusion, speeding the process of
identity verification.
Glasses or contact lens use does
not affect it.
Most eye surgeries do not change
the iris. In the few, such as
iridotomy and iridectomy, both
associated with glaucoma, reenrollment may be necessary.
These technologies can be
integrated into existing business
systems easing the installation
requirements.
In this instance, retina scanning
has an advantage over iris
scanning in that retina scanning
utilizes a very compact template.
Retina scanning requires 96 bytes
while iris scanning uses 512
bytes.
A greater number of templates
can be stored in a standalone
device if retina scanning is
employed.
Advantages:
The iris of the eye has been
described as the ideal part of the
human body for
biometric
identification for several reasons:
• It is an internal organ that is well
protected against damage and
wear by a highly transparent and
sensitive membrane (the cornea).
This distinguishes it from
fingerprints, which can be
difficult to recognize after years
of certain types of manual labor.
• The iris is mostly flat and its
geometric configuration is only
controlled
by
two
complementary muscles (the
sphincter pupillae and dilator
pupillae), which control the
diameter of the pupil. This makes
the iris shape far more
predictable than, for instance,
that of the face.
• The iris has a fine texture that –
like fingerprints – is determined
randomly during embryonic
gestation.
Even
genetically
identical
individuals
have
completely independent iris
textures, whereas DNA (genetic
"fingerprinting") is not unique
for the about 1.5% of the human
population
who
have
a
genetically
identical
monozygotic twin.
• An iris scan is similar to taking a
photograph and can be performed
from about 10 cm to a few
meters away. There is no need
for the person to be identified to
touch any equipment that has
recently been touched by a
stranger, thereby eliminating an
objection that has been raised in
some cultures against finger-print
scanners, where a finger has to
touch a surface, or retinal
scanning, where the eye can be
12. •
brought very close to a lens (like
looking into a microscope lens).
The only currently commercially
deployed
iris
recognition
algorithm,
John
Daugman's
IrisCode, has an unprecedented
false match rate (better than
10−11). Not a single false match
has ever been reported for this
algorithm, which has already
been used to cross-compare more
than 200 billion combinations of
iris pairs as part of the
immigration procedures in the
United Arab Emirates.
Disadvantages:
•
•
•
•
Iris scanning is a relatively new
technology and is incompatible
with the very substantial
investment
that
the
law
enforcement and immigration
authorities of some countries
have already made into fingerprint recognition.
Iris recognition is very difficult
to perform at a distance larger
than a few meters and if the
person to be identified is not
cooperating by holding the head
still and looking into the camera.
As with other photographic
biometric
technologies,
iris
recognition is susceptible to poor
image quality, with associated
failure to enroll rates.
As with other identification
infrastructure (national residents
databases, ID cards, etc.), civil
rights activists have voiced
concerns that iris-recognition
technology
might
help
governments to track individuals
beyond their will.
Security considerations:
Like with most other biometric
identification technology, a still not
satisfactorily solved problem with iris
recognition is the problem of "live tissue
verification". The reliability of any
biometric identification depends on
ensuring that the signal acquired and
compared has actually been recorded
from a live body part of the person to be
identified, and is not a manufactured
template. Many commercially available
iris recognition systems are easily fooled
by presenting a high-quality photograph
of a face instead of a real face, which
makes such devices unsuitable for
unsupervised applications, such as door
access-control systems. The problem of
live tissue verification is less of a
concern in supervised applications (e.g.,
immigration control), where a human
operator supervises the process of taking
the picture.Methods that have been
suggested to provide some defence
against the use of fake eyes and irises
include:
• Changing
ambient
lighting
during
the
identification
(switching on a bright lamp),
such that the pupillary reflex can
be verified and the iris image be
recorded at several different
pupil diameters
• Analysing the 2D spatial
frequency spectrum of the iris
image for the peaks caused by
the printer dither patterns found
on commercially available fakeiris contact lenses
• Using spectral analysis instead of
merely monochromatic cameras
13. •
•
•
•
to distinguish iris tissue from
other material
Observing the characteristic
natural movement of an eyeball
(measuring nystagmus, tracking
eye while text is read, etc.)
Testing for retinal retroreflection
(red-eye effect)
Testing for reflections from the
eye's four optical surfaces (front
and back of both cornea and lens)
to verify their presence, position
and shape
Using 3D imaging (e.g., stereo
cameras) to verify the position
and shape of the iris relative to
other eye features .
CONCLUSION
Highly accurate, positive personal
recognition is feasible today using the
iris of the human eye. This unique and
complex organ, which has more
dimensions (Measures) of variation than
any other biometric feature currently in
use, remains stable throughout a lifetime
and is readily available for sampling in a
nonintrusive way. And has the speed
required minimizing user frustration
when accessing company systems. The
process uses simple and non-threatening
video technology to take images of the
iris, digitize the features, and create a
512-byte code, which is then compared
against an entire database in less than
two seconds. Recognitions can then be
used to control access and entry, to
provide recognition information to an
existing entry control system or for any
other
purpose
where
positive
identification is needed. Recent testing,
under U.S. Government controlled
conditions,
in
three
real-world
environments, and in a variety of
operational applications have proven the
practicality and feasibility of the
extremely accurate iris recognition for
any
function
requiring
positive
recognition.
