IRIS RECOGNITION

Seminar Report

Submitted in partial fulfillment of

Master of Computer Application V Semester

MCA V Semester

Session : 2011-2014

Submitted to Submitted by

Dr. Vaibhav Gupta Ravi Joshi

(Asst.Prof, Department of

Computer Science)

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DEPARTMENT OF COMPUTER SCIENCE

LACHOO MEMORIAL COLLEGE OF SCIENCE & TECHNOLOGY

JODHPUR, RAJASTHAN.

CERTIFICATE

This is to certify that Seminar entitled “Iris Recognition” has been analyzed by:

Ravi Joshi

In Partial fulfillment of MCA V Semester, under our supervision and guidance

Dr. Vaibhav Gupta Prof.(Dr.) Rajeev Mathur

(Mentor & Guide) (Director)

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Acknowledgement

The successful completion of this report rest on the shoulders of many persons who have helped us directly or indirectly. We wish to take this opportunity to express our gratitude to all those, without whose help, completion of report would have been difficult.

We are grateful to Prof. (Dr.) Rajeev Mathur (Director, Computer Science Department, LMC), for his proper guidance, encouragement and support. The help by all the other faculty members is also greatly acknowledged.

We express deep & sincere gratitude to Dr. Vaibhav Gupta whose guidance, encouragement and affectionate pressure helped us in completing this report. His inspiration and encouragement, suggestion and very constructive criticism have contributed immensely to the evolution of our ideas on the subject.

Lastly we show respect to my parents for their Love and Blessing which helped us in completing this venture.

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TABLE OF CONTENTSACKNOWLEDGEMENT....................................................................................................................ii

TABLE OF CONTENTS.....................................................................................................................iii

LIST OF FIGURES..............................................................................................................................iv

ABSTRACT............................................................................................................................................1

Biometric technology.............................................................................................................................2

Introduction............................................................................................................................2

Types of biometrics................................................................................................................3

Choosing a biometric authentication......................................................................................3

Iris recognition.......................................................................................................................................5

Introduction............................................................................................................................5

History....................................................................................................................................5

Human iris..............................................................................................................................6

Working of iris recognition system........................................................................................8

Features of iris recognition.....................................................................................................9

Iris recognition process.......................................................................................................................10

Image acquisition..................................................................................................................10

Image localization................................................................................................................13

Iris segmentation..................................................................................................................15

Normalization.......................................................................................................................15

Feature extraction.................................................................................................................16

Matching...............................................................................................................................16

Application...........................................................................................................................................18

Advantages and disadvantages of iris recognition............................................................................21

Advantages:..........................................................................................................................21

Disadvantages.......................................................................................................................24

Conclusion............................................................................................................................................26

References.............................................................................................................................................28

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ABSTRACT The Iris Recognition System is very interesting and today it is widely used for the security

point of view. Iris recognition gives accurate results. It never takes false value, so it is really

very good for security purpose. Iris recognition is amongst the most robust and accurate

biometric technologies available in the market today with existing large scale applications

supporting databases in excess of millions of people.

Daugman is the first one to give an algorithm for iris recognition. His algorithm is based on

Iris Codes. For the preprocessing step i.e., inner and outer boundaries of the iris are located.

Integro-differential operators are then used to detect the centre and diameter of the iris, then

the pupil is also detected using the differential operators, for conversion from Cartesian to

polar transform, rectangular representation of the required area is made. Boashash and Boles

have given a new approach based on zero-crossings. They first localized and normalized the

iris by using edge detection and other well known computer vision algorithms. The zero-

crossings of the wavelet transform are then calculated at various resolution levels over

concentric circles on the iris. The resulting one dimensional (ID) signals are then compared

with the model features using different dissimilarity function. This system can handle noisy

conditions as well as variations in illumination. A new algorithm has been proposed to extract

the features of iris signals by Multi-resolution Independent Component Identification (M-

ICA). It provides good properties to represent signals with time frequency. An efficient

algorithm phase-based image matching is an image matching technique which uses only the

phase components in 2D DFTs (Two-Dimensional Discrete Fourier Transforms) of given

images. The technique has been successfully applied to high accuracy image registration

tasks for computer vision applications, where estimation of sub-pixel image translation is a

major concern. Wildes proposed iris recognition based on texture analysis. High quality iris

images was captured using silicon intensified target camera coupled with a standard frame

grabber and resolution of 512x480 pixels. The limbus and pupil are modeled with circular

contours which are extended to upper and lower eyelids with parabolic arcs.

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Biometric technology

Introduction

In today’s information age it is not difficult to collect data about an individual and to use that

information to control the individual. With the development of technology, it is difficult to

maintain the levels of privacy. In this context, data security has become an invetible feature.

The three main types of authentication are “something you know (such as a password),

something you have (such as a card or token), and something you are

(biometric)”.Conventional method of identifications based on ID card ,exclusive knowledge

like social security number and passwords are not reliable.ID card can be stolen and

password can be forgotten. Unauthorized person can broke in to the account with little effort.

So there is a need of denial of service to classified data by unauthorized people.

A biometric system provides automatic identification of a person based on

physiological and behavioral characteristics. These techniques which use physical data is

receiving attention as a personal authentication method. This data is unique to the individual

and remains so throughout one’s life. Some common physical characteristics used for

identification include fingerprints, palm prints, facial features, voice, hand geometry,

handwriting, retina and the one presented in this report, the iris.

Biometric system works by capturing a sample for feature such as taking a digital

colored image for face recognition or palmprint. The sample image is then transformed by

using some mathematical function in to biometric template. This template will provide a

normalized, efficient representation of the feature, which is compared with other template to

verify the identity. A good biometric is characterized by use of feature that are unique, stable

and chance of any two people having the same characteristic is minimal.

Out of all the various physical characteristics available, irises are one of the more

accurate and convenient physiological characteristics because iris is protected behind the

eyelid, cornea, and aqueous humor means that, unlike other biometrics such as fingerprints,

the likelihood of damage is minimal. The probability of finding two people with identical iris

patterns is considered to be approximately 1 in 1052(population of the earth is of the order

1010). There is no need for the person being identified to touch any equipment because iris

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scan is similar to taking a photograph and can be performed from about 10 cm to a few

meters away. The pattern of iris never changes with age.

Advantages of biometrics

Easier fraud detection

Better than password/PIN or smart cards

No need to memorize passwords

Requires physical presence of the person to be identified

Unique physical or behavioral characteristic

Cannot be borrowed, stolen, or forgotten

Cannot leave it at home

Types of biometrics

a) Physical Biometrics

Finger print Recognition

Facial Recognition

Hand Geometry

IRIS Recognition

DNA

Retinal Scanning

b) Behavioral Biometrics

Speaker Recognition

Signature

Keystroke

Walking style

Choosing a biometric authentication

Before choosing a biometric user authentication solution, an organization should evaluate its

needs carefully. The following list includes items that should be considered—the order of

importance depends on the environment and level of security needed.

Level of security required

Accuracy

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Identification

Enrollment

Feature Set

Stored Feature Set

Accept/reject

Stored template

MatcherTemplate generator

Feature extractor

Pre-processing

Sensor

Cost and implementation time

User acceptance

Figure 1: Evaluation Simplified block diagram representation of a biometric system

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Iris recognition

Introduction

Iris recognition is a method of biometric authentication that recognizes a person by pattern of

the iris. Of all the biometric technologies used for human authentication today, it is generally

conceded that iris recognition is the most accurate. The purpose of ‘Iris Recognition’, a

biometrical based technology for personal identification and verification, is to recognize a

person from his/her iris prints. In fact, iris patterns are characterized by high level of stability

and distinctiveness. Each individual has a unique iris. The difference even exists between

identical twins and between the left and right eye of the same person. Irises are also stable;

unlike other identifying characteristics that can change with age, the pattern of one's iris is

fully formed by ten months of age and remains the same for the duration of their lifetime. Iris

recognition is rarely impeded by glasses or contact lenses and can be scanned from 10cm to a

few meters away.

The probability of finding two people with identical iris patterns is considered to be

approximately 1 in 1052(population of the earth is of the order 1010). Not even one-egged

twins or a future clone of a person will have the same iris patterns. The iris is considered to

be an internal organ because it is so well protected by the eyelid and the cornea from

environmental damage. Iris recognition is the most precise and fastest of the biometric

authentication method.

History

In 1936, ophthalmologist Frank Burch proposed the concept of using iris patterns as a

method to recognize an individual.

In 1985, Drs. Leonard Flom and Aran Sair, ophthalmologists, proposed the concept that no

two irises are alike, and were awarded a patent for the iris identification concept in 1987. Dr.

Flom approached Harvard Professor Dr. John Daugman to develop an algorithm to automate

identification of the human iris.

In 1993, the Defense Nuclear Agency began work to test and deliver a prototype unit,

which was successfully completed by 1995 due to the combined efforts of Drs. Flom, Sair,

and Daugman.

In 1994, Dr. Daugman was awarded a patent for his automated iris recognition algorithms.

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In 1995, the first commercial products became available.

In 2005, the broad patent covering the basic concept of iris recognition expired, providing

marketing opportunities for other companies that have developed their own algorithms for iris

recognition.

The patent on the IrisCodes implementation of iris recognition developed by Dr. Daugman

will not expire until 2011.

Human iris

The iris is a thin circular diaphragm, which lies between the cornea and the lens of the human

eye. A front-on view of the iris is shown in Figure 2.1. The iris is perforated close to its

centre by a circular aperture known as the pupil. The function of the iris is to control the

amount of light entering through the pupil, and this is done by the sphincter and the dilator

muscles, which adjust the size of the pupil. The average diameter of the iris is 12 mm, and the

pupil size can vary from 10% to 80% of the iris diameter.

The iris consists of a number of layers; the lowest is the epithelium layer, which

contains dense pigmentation cells. The stromal layer lies above the epithelium layer, and

contains blood vessels, pigment cells and the two iris muscles. The density of stromal

pigmentation determines the color of the iris. The externally visible surface of the multi-

layered iris contains two zones, which often differ in color. An outer ciliary zone and an inner

pupillary zone, and these two zones are divided by the collarette – which appears as a zigzag

pattern. Color is not used in iris recognition technology. Instead, the other visible features

such as the connective tissue, cilia, contraction furrows, crypts, rings and corona distinguish

one iris from another.

The iris is divided into two major regions:

1. The pupillary zone is the inner region whose edge forms the boundary of the pupil.

2. The ciliary zone is the rest of the iris that extends to its origin at the ciliary body.

Iris surface features

1. The pupillary ruff is a series of small ridges at the pupillary margin formed by the

continuation of the pigmented epithelium from the posterior surface.

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Figure 2: Structure of the iris

2. The Circular contraction folds, also known as contraction furrows, are a series of

circular bands or folds about midway between the collaret and the origin of the iris. These

folds result from changes in the surface of the iris as it dilates.

3. Crypts at the base of the iris are additional openings that can be observed close to the

outermost part of the ciliary portion of the iris.

Figure 3: Internal structure of iris

Features of iris

The iris has many features that can be used to distinguish one iris from another. One of the

primary visible characteristics is the trabecular meshwork, a tissue which gives the

appearance of dividing the iris in radial fashion that is permanently formed by the eighth

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Eye Image acquisitionSegmentation

Normalization

Feature Extraction MatchingLocalization

month of gestation. During the development of the iris, there is no genetic influence on it, a

process known as chaotic morphogenesis that occurs during the seventh month of gestation,

which means that even identical twins have different irises.

The fact that the iris is protected behind the eyelid, cornea, and aqueous humor means

that, unlike other biometrics such as fingerprints, the likelihood of damage or abrasion is

minimal. The iris is also not subject to the effects of aging which means it remains in a stable

form about the age of one until death. The use of glasses or contact l lenses (colored or clear)

has little effect on the representation of the iris and hence does not interfere with the

recognition technology.

Working of iris recognition system

Image processing techniques can be employed to extract the unique iris pattern from a

digitized image of the eye, and encode it into a biometric template, which can be stored in a

database. This biometric template contains an objective mathematical representation of the

unique information stored in the iris, and allows comparisons to be made between templates.

When a subject wishes to be identified by iris recognition system, their eye is first

photographed, and then a template created for their iris region. This template is then

compared with the other templates stored in a database until either a matching template is

found and the subject is identified, or no match is found and the subject remains unidentified.

Figure 4: Iris recognition system

It includes Three Main Stages:

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1) Image Acquisition- Acquiring image of the eye.

2) Image Preprocessing- Separation of iris part from the image of eye.

3) Iris Pattern Matching- Comparing the saved pattern with that of the present one.

Process of IRIS Recognition and Identification System can be summarized as follows:-

Features of iris recognition

Measurable Physical Features: 250 degrees of freedom,250 non-related unique features

of a person’s iris

Unique: Every iris is absolutely unique. No two iris are the same

Stable: iris remains stable from 1styear till death.

Accurate: Iris recognition is the most accurate of the commonly used biometric

technologies.

Fast: Iris recognition takes less than 2 seconds. 20 times more matches per minute than its

closest competitor.

Non-Invasive: No bright lights or lasers are used in the imaging and iris authentication

process.

Flexible: iris recognition technology easily integrates into existing security systems

Reliable: iris pattern is distinctive and is not susceptible to theft, loss or compromise.

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Iris recognition process

Image acquisition

The most important challenge of iris recognition system is to capture high quality image. The

image of the iris can be captured using a standard camera using both visible and infrared light

and may be either a manual or automated procedure. In order to accomplish this, use CCD

camera. The resolution is set to 640x480; the type of image to jpeg, and the mode to white

and black. The camera is situated normally between half a meters to one meter from subject.

In the manual procedure, the user needs to adjust the camera to get the iris in focus and needs

to be within six to twelve inches of the camera. This process is much more manually

intensive and requires proper user training to be successful. The automatic procedure uses a

set of cameras that locate the face and iris automatically thus making this process much more

user friendly.

With an average diameter of 12mm, a camera must have enough resolution to capture

the details of the iris pattern (collarets patterns). Some commercial products solve the

problem with a dual lens system. One lens is a wide angle that serves the purpose of

localization of the iris in the scene. The other with a higher zoom factor (tele) is driven by the

iris localization system, narrowing the focus only to the target and therefore taking a high

resolution snapshot of the iris.

Another problem is illumination. The collaret’s are very similar to mountain ranges,

with valleys, dips and peaks. The illumination angle will determine the dark and light parts of

the image. It is very important that one system implements consistent illumination, on the

contrary the same iris may generate two different classes under two different illumination

angles. Also, the pupil is an open door to the retina, one of the most sensitive organs of our

body, and extra care.

Given that the iris is a relatively small (typically about 1 cm in diameter), dark object

and that human operator are very sensitive about their eyes, and this matter requires careful

engineering. Several points are of particular concern. First, it is desirable to acquire images of

the iris with sufficient resolution and sharpness to support recognition. Second, it is important

to have good contrast in the interior iris pattern without resorting to a level of illumination

that annoys the operator, i.e., adequate intensity of source (W/cm ) constrained by operator

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comfort with brightness (W/sr-cm ). Third, these images must be well framed (i.e., centered)

without unduly constraining the operator (i.e., preferably without requiring the operator to

employ an eye piece, chin rest, or other contact positioning that would be invasive). Further,

as an integral part of this process, artifacts in the acquired images (e.g., due to specular

reflections, optical aberrations, etc.) should be eliminated as much as possible.

Concerns on the image acquisition rigs:

Obtained images with sufficient resolution and sharpness

Good contrast in the interior iris pattern with proper illumination

Well centered without unduly constraining the operator

Artifacts eliminated as much as possible

The Daugman system captures images with the iris diameter typically between 100

and 200 pixels from a distance of 15–46 cm using a 330-mm lens. Similarly, the Wildes et al.

system images the iris with approximately 256 pixels across the diameter from 20 cm using

an 80-mm lens.

The positioning of the iris for image capture is concerned with framing the entire iris

in the camera’s field of view with good focus. Both the Daugman and Wildes et al. systems

require the operator to self-position his eye region in front of the camera. Daugman’s system

provides the operator with live video feedback via a miniature liquid crystal display placed in

line with the camera’s optics via a beam splitter. This allows the operator to see what the

camera is capturing and to adjust his position accordingly. 2 Light emerging from the circular

polarizer will have a particular sense of rotation. When this light strikes a specularly

reflecting surface (e.g., the cornea), the light that is reflected back is still polarized but has

reversed sense. This reversed-sense light is not passed through the camera’s filter and is

thereby blocked from forming an image. In contrast, the diffusely reflecting parts of the eye

(e.g., the iris) scatter the impinging light. This light is passed through the camera’s filter and

is subsequently available for image formation. Interestingly, a similar solution using crossed

polarizer’s (e.g., vertical at the illuminant and horizontal at the camera) is not appropriate for

this application: the birefringence of the eye’s cornea yields a low-frequency artifact in the

acquired images .During this process, the system is continually acquiring images. Once a

series of images of sufficient quality is acquired, one is automatically forwarded for

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Lens

Camer

aB

eam Splitter

Frame Gabber

LC

D

Disp

lay

Lens

Lig-ht

subsequent processing. Image quality is assessed by looking for high contrast edges marking

the boundary between the iris and the sclera.

In contrast, the Wildes et al. system provides a reticle to aid the operator in

positioning. In particular, a square contour is centered around the camera lens so that it is

visible to the operator. Suspended in front of this contour is a second, smaller contour of the

same shape. The relative sizes and positions of these contours are chosen so that when the

eye is in an appropriate position, the squares overlap and appear as one to the operator. As the

operator maneuvers, the relative misalignment of the squares provides continuous feedback

regarding the accuracy of the current position. Once the operator has completed the

alignment; he activates the image capture by pressing a button.

Figure 5: The Daugman image-acquisition rig

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Eye Lens

Figure 6: Wildes et al. image acquisition rig

Image localization

Image acquisition of the iris cannot directly yield an image containing only the iris. Rather,

image acquisition will capture the iris as part of a larger image that also contains data derived

from the immediately surrounding eye region. Therefore, before performing iris pattern

matching, it is important to localize that portion of the acquired image that corresponds to an

iris. In particular, it is necessary to localize that portion of the image derived from inside the

limbus (the border between the sclera and the iris) and outside the pupil. Further, if the

eyelids are occluding part of the iris, then only that portion of the image below the upper

eyelid and above the lower eyelid should be included. Typically, the limbic boundary is

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Camera

Light

Light

Edge of alignment square

Edge of alignment square

Circular Polarizer

Diffuser

Circular Polarizer

Diffuser

Frame Gabber

imaged with high contrast, owing to the sharp change in eye pigmentation that it marks. The

upper and lower portions of this boundary, however, can be occluded by the eyelids.

Since the picture was acquired using an infrared camera the pupil is a very distinct

black circle. The pupil is in fact so black relative to everything else in the picture simple edge

detection should be able to find its outside edge very easily. Like the pupillary boundary,

eyelid contrast can be quite variable depending on the relative pigmentation in the skin and

the iris. The eyelid boundary also can be irregular due to the presence of eyelashes.

Desired characteristics of iris localization:

Sensitive to a wide range of edge contrast

Robust to irregular borders and Capable of dealing with variable occlusions

The Daugman and Wildes et al. iris recognition systems perform iris localization.

Both of these systems make use of first derivatives of image intensity to signal the location of

edges that correspond to the borders of the iris. Here, the notion is that the magnitude of the

derivative across an imaged border will show a local maximum due to the local change of

image intensity. Also, both systems model the various boundaries that delimit the iris with

simple geometric models.

The Wildes et al. system also explicitly models the upper and lower eyelids with

parabolic arcs, whereas the Daugman system simply excludes the upper- and lower-most

portions of the image, where eyelid occlusion is expected to occur. In both systems, the

expected configuration of model components is used to fine tune the image intensity

derivative information.

In particular, for the limbic boundary, the derivatives are filtered to be selective for

vertical edges. This directional selectivity is motivated by the fact that even in the face of

occluding eyelids, the left and right portions of the limbus should be visible and oriented near

the vertical (assuming that the head is in an upright position). Similarly, the derivatives are

filtered to be selective for horizontal information when locating the eyelid borders. In

contrast, since the entire (roughly circular) papillary boundary is expected to be present in the

image, the derivative information is used in a more isotropic fashion for localization of this

structure. In practice, this fine tuning of the image information has proven to be critical for

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accurate localization. For example, without such tuning, the fits can be driven astray by

competing image structures (e.g., eyelids interfering with limbic localization, etc.).

Iris segmentation

Segmentation is to isolate the actual iris region in a digital eye image. Image can be viewed

as depicting a scene composed of different regions, objects, etc. Image Segmentation is the

process of decomposing the image into these regions and objects by associating or labeling

each pixel with the object that it corresponds to. Hence, segmentation subdivides an image

into its constituent regions or objects. The first stage of iris recognition is to isolate circular

iris region. Iris is isolated using concentric circles, one circle is defined by the edge between

sclera and iris and other is defined by the edge between pupil and iris. The process involves

the extraction of circular boundaries of pupil and iris from the edge map using Circular

Hough Transform (CHT). Usually, pre-segmentation process involves blurring the image

using low-pass filter to remove the noise.

Normalization

Once the iris region is successfully segmented from an eye image, the next stage is to

transform the iris region so that it has fixed dimensions in order to allow comparisons. The

normalization process will produce iris regions, which have the same constant dimensions, so

that two photographs of the same iris under different conditions will have characteristic

features at the same spatial location. The dimensional inconsistencies between eye images are

mainly due to the stretching of the iris caused by pupil dilation from varying levels of

illumination. Other sources of inconsistency include, varying imaging distance, rotation of

the camera, head tilt, and rotation of the eye within the eye socket. The normalization process

will produce iris regions, which have the same constant dimensions, so that two photographs

of the same iris under different conditions will have characteristic features at the same spatial

location. In normalization, the iris circular region is transformed to a rectangular region with

a fixed size. Another point of note is that the pupil region is not always concentric within the

iris region, and is usually slightly nasal. This must be taken into account if trying to

normalize the ‘doughnut’ shaped iris region to have constant radius.

Robust representations for pattern recognition must be invariant to changes in the

size, position and orientation of the patterns. In the iris biometric compass, this means that a

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representation of the iris data invariant to changes in the distance between the eye and the

capturing device, in the camera optical magnification factor and in the iris orientation, caused

by torsional eye rotation and camera angles, must be accomplished. The irises captured from

the different people have different sizes. The normalization algorithm always depends on the

algorithm of feature vector extraction and match.

Techniques available for normalization are: (1) Daugman rubber sheet model

(2) Virtual circles

Feature extraction

Iris provides abundant texture information. Feature selection and extraction is to find out the

important features to perform matching. As we know, the visible features of an iris are ciliary

processes, contraction furrows, crypts, rings, cornea, and freckles and so on. How to set a

model to extract the feature of different irises and match them is especially important for it

determines the results of the whole system directly. A feature vector is formed which consists

of the ordered sequence of features extracted from the various representation of the iris

images.

In order to provide accurate recognition of individuals, the most discriminating

information present in an iris pattern must be extracted. Only the significant features of the

iris must be encoded so that comparisons between templates can be made. Most iris

recognition systems make use of a band pass decomposition of the iris image to create a

biometric template.

The iris has a particularly interesting structure and provides abundant texture

information. Feature extraction is a crucial part in any iris recognition system since good

identification rates are directly related to the uniqueness and variability of the extracted

features used to distinguish between different biometric templates.

Matching

Using one of the previously described feature extraction schemes, an iris image is processed

and transformed into a unique representation within the feature space. In order to see if two

iris templates match (i.e. extracted from the same eye) which involves making an

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accept/reject decision, a distance measure is indeed necessary to measure the closeness of a

match. The template that is generated in the feature encoding process will need a

corresponding matching metric system, which gives a measure of similarity between two iris

templates.

This metric system should give one range of values when comparing templates generated

from the same eye, known as intra-class comparisons, and another range of values when

comparing templates created from different irises, known as inter-class comparisons. These

two cases should give distinct and separate values, so that a decision can be made with high

confidence as to whether two templates are from the same iris, or from two different irises.

For example, some widely used methods in the iris recognition field are the Hamming

distance (HD), the normalized correlation (NC) and the weighted Euclidean distance (WED).

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Application

Iris recognition is forecast to play a role in a wide range of other applications in which a

person's identity must be established or confirmed. These include electronic commerce,

information security, entitlements authorization, building entry, automobile ignition, forensic

and police applications, network access and computer applications, or any other transaction in

which personal identification currently relies just on special possessions or secrets (keys,

cards, documents, passwords, PINs). Because of its reliability and ease of use, Iris

recognition technology is gaining popularity across the globe in areas such as public safety,

aviation, education and health care.

ATM’s and iris recognition: in U.S many banks incorporated iris recognition technology

into ATM’s for the purpose of controlling access to one’s bank accounts. After enrolling once

(a “30 second” process), the customer need only approach the ATM, follow the instruction to

look at the camera, and be recognized within 2-4 seconds. The benefits of such a system are

that the customer who chooses to use bank’s ATM with iris recognition will have a quicker,

more secure transaction.

Tracking Prisoner Movement: The exceptionally high levels of accuracy provided by iris

recognition technology broadens its applicability in high risk, high-security installations. Iris

scan has implemented their devices with great success in prisons in Pennsylvania and Florida.

By this any prison transfer or release is authorized through biometric identification. Such

devices greatly ease logistical and staffing problems. Applications of this type are well suited

to iris recognition technology. First, being fairly large, iris recognition physical security

devices are easily integrated into the mountable, sturdy apparatuses needed or access control,

The technology’s phenomenal accuracy can be relied upon to prevent unauthorized release or

transfer and to identify repeat offenders re-entering prison under a different identity.

Law enforcement agencies: Law enforcement agencies in the United States began using it in

1994 when the Lancaster County Prison in Pennsylvania became the first correctional facility

to employ the technology for prisoner identification. In Berkshire County, the technology is

used in the newly built Berkshire County Jail as a security check for employees. · 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

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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.

The Charlotte Airport: The Charlotte Airport in North Carolina and the Flughafen

Frankfort Airport in Germany allow frequent passengers to register their iris scans in an effort

to streamline boarding procedures.

Healthcare solutions: In addition to the government and transportation markets, iris

recognition is playing an emerging role in healthcare. Healthcare solutions based on iris

recognition protect access to patient medical records at hospitals in locations such as

Washington, D.C., Pennsylvania and

Germany, infant nursing stations: Alabama. Also, in Germany, infant nursing stations are

equipped with iris recognition to ensure that only parents, doctors and nurses have access to

the room that holds newborn children and to avoid potential abductions.

· The technology was recently introduced by the United Nations in Afghanistan, where iris

recognition helps to distribute a onetime grant of human aid to refugees who wish to return to

their homeland. Four voluntary repatriation centers in Pakistan are equipped with cameras to

process the refugees anonymously and quickly so that they can receive funds, food and travel

supplies.

· In UK project IRIS (Iris Recognition Immigration System), over 200,000 passengers have

bypassed immigration control and passport presentation by instead being registered in this

system for automated border-crossing using iris recognition

Computer login: The iris as a living password.

National Border Controls: The iris as a living password.

Telephone call charging without cash, cards or PIN numbers.

Ticket less air travel.

Premises access control (home, office, laboratory etc.).

Driving licenses and other personal certificates.

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Entitlements and benefits authentication.

Forensics, birth certificates, tracking missing or wanted person

Credit-card authentication.

Automobile ignition and unlocking; anti-theft devices.

Anti-terrorism (e.g.:— suspect Screening at airports)

Secure financial transaction (e-commerce, banking).

Internet security, control of access to privileged information.

“Biometric—key Cryptography “for encrypting/decrypting messages.

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Advantages and disadvantages of iris recognition

Advantages:

Accuracy

In a comprehensive study conducted by Britain’s National Physical Laboratory (NPL) iris

recognition technology decisively outperformed six other biometrics systems –facial

recognition, fingerprint, hand geometry, and vein and voice recognition. Tested to positively

identify users in an office environment iris recognition technology registered a false match

rate of zero in 2,735,529 comparisons and a 0.0 percent failure-to-acquire rate. Iris

recognition had a false rejection rate of 0.0188, the lowest of all systems rested. Four of the

other biometrics systems registered a false rejection rate of 10- 25 percent. Simply put, iris

recognition is the most accurate form of identification known to man. More accurate than

even DNA matching. Due to the process of chaotic morphogenesis, every iris is unique to a

degree that the probability of 2 irises being identical is 1 in 10 to the power of

78.Additionally, the iris recognition system captures over 240 'degrees of freedom' or points

of interest. This is more data than is collected by most hand, face and voice recognition

systems combined. Other systems have the potential to be fooled by replicas and duplicates.

The iris cameras have built in countermeasures to ensure that it is a live eye being presented

meaning that high-quality 2-D or 3-D reproductions pose no threat to iris recognition

systems. 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.

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 reallife applications this

translates into an identity decision being made in seconds. The enrollment process is also

speedily accomplished, typically in three minutes or less. The iris recognition system is

capable of making a match from a database of over 1 million records in less than a second.

Conversely, fingerprint, hand and voice systems are challenged by large databases. Not only

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does the time taken to register a match increase, but also the accuracy of the system falls

unlike iris recognition.

Safety and Perceived Invasiveness

The co-operation of the user involves the enrollee being still, and looking at a certain spot.

iris scanning is less difficult than retina scanning . The devices capture images of the eye

from a comfortable distance without bright lights or lasers. The IrisCode is hashed and

encrypted as a security measure to prevent theft. If a person feels their recognition patterns

have been compromised, re-enrollment is possible an infinite number of times by using a

permuted IrisCode

Identification vs. Verification: Iris recognition identifies people rather than verifying their

identity. Verification asks; is this person who they say they are? This is one-to-one matching

which means a person must first suggest their identity through a password, card or name and

the system then seeks to determine whether or not there is a match between the suggested and

true identities. Identification asks; who is this person? This is one-to-many matching meaning

that the person is not required to carry anything or volunteer any information. The system

simply captures the iris image, searches the entire database and either finds their identity or

reports that they are unknown. This is obviously a much more powerful form of

authentication as no information is required from the user.

Stability: The iris image remains stable from the age of about 10 months up until death. It is

an internal organ that is well protected against damage and wear by a highly transparent and

sensitive membrane (the cornea).This means that an iris image need only be captured once

and does not need to be updated. Other biometric measures change over time. Conversely,

barring surgery or extensive trauma, the iris template does not change over time.

Non-invasive: Users wearing gloves, protective wear, glasses, safety goggles and even

contact lenses can operate iris recognition systems. No contact is required with a touch pad or

screen meaning that iris recognition is ideal in environments where hygiene is at a premium.

It is also important to note that iris recognition is a completely separate technology to retinal

scanning. No bright lights or lasers are beamed into the eye; only a digital photograph is

taken. This means that not only is iris recognition the most accurate biometric technology, it

is also the safest.

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Fool Proof: 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 falserejection rate, whereas iris

scanning systems boast rates at the 0 percent level.

Iris recognition is currently in use Highly protected, internal organ of the eye

Externally visible; patterns imaged from a distance

Iris patterns possess a high degree of randomness

Variability; 244 degrees-of-freedom

Entropy; 3.2 bits per square-millimeter

Uniqueness: set by combinatorial complexity

Changing pupil size confirms natural physiology

Pre-natal morphogenesis (7th month of gestation)

Limited genetic penetrance of iris pattern

Pattern apparently stable throughout life

Encoding and decision-making are tractable

Image analysis and encoding time: 1second

Decidability index (d-prime): d’=7.3 to 11.4

Search speed: 100000 Iris Codes per second

Iris is relatively flat.

Can exhaustively search an Iris database of 100,000 in close to one second.

Unaffected by Viewing angle (transformations are affine and reversible).

Iris diameter of 200 pixels gives long-term stable information.

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Figure 7: identifying the mystery woman

Iris recognition system is also finding unexpected applications. The best know example

involved using iris recognition to confirm the identification of a mysterious young afghan

woman named Sharbat Gula originally photographed by Steve McCurry in 1984.Some 18

years later, McCurry photographed Sharbat Gula in Afghanistan .At the behest of National

Geographic, Dr.John Dougman,developer of the Iris recognition system, then compared the

irises in the photographs using his algorithms. He concluded that the eyes were a match.

Disadvantages

1. The iris is a very small organ to scan from a distance.

2. It is a moving target and can be obscured by objects such as the eyelid and eye lashes.

3. The camera here needs to have the correct amount of illumination. The system linked

by the camera is currently only capturing images in a monochrome format.

4. Generally more expensive than other biometric technologies it 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

finger-print recognition.

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5. Small target (1 cm) to acquire from a distance (1 m).It 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. The scanner needs to be

adjusted for height differences in people.

6. Illumination should not be visible or bright .As with other photographic biometric

technologies, iris recognition is susceptible to poor image quality, with associated

failure to enroll rates.

7. 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.

8. In general, multiple scanners run from a single central processor unit. (PC) .If the PC

fails then several units are affected rather than a single reader · Blind persons may

have difficulty in getting themselves aligned with the iris camera at arm's length,

because some such systems rely on visual feedback via a mirror or LCD display to

guide the user into alignment with the camera

Deforms non-elastically as pupil changes size

Partially occluded by eyelids, often drooping

Small target (1 cm) to acquire from a distance (1m)

Located behind a curved, wet, reflecting surface

Obscured by eyelashes, lenses, reflections

Partially occluded by eyelids, often drooping

Deforms non-elastically as pupil changes size

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Conclusion

Despite its challenges, iris recognition is gaining popularity as a robust and reliable biometric

technology. 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 non-intrusive way. And has the speed

required minimizing user frustration when accessing company systems. The iris’s complex

texture and its apparent stability hold tremendous promise for leveraging iris recognition in

diverse application scenarios, such as border control, forensic investigations, and

cryptosystems. Recent efforts in machine vision have yielded automated systems that take

strides toward realizing this potential. As currently instantiated, these systems are relatively

compact and efficient and have shown promising performance in preliminary testing. The use

of other ocular features and facial attributes along with the iris modality could enable

biometric recognition at a distance with good matching accuracy. The future of iris-based

recognition looks bright, particularly in military applications that demand the rapid

identification of individuals in dynamic environments.

Iris recognition systems have made tremendous inroads over the past decade, but

work remains to improve their accuracy in environments characterized by unfavorable

lighting, large stand-off distances, and moving subjects. This by far remains the technology

of the future. This biometric technology proves out to be unique for every individual

including the twins and this makes it to take a greater prominence over the others. As with

almost every new technology that seeks to find its place in everyday life, iris recognition has

both the potential to be a convenience enhancer including an access enhancer. Because it

allows hands-free, automatic, rapid and reliable identification of persons, it can facilitate

access for persons unable to engage in the standard mechanical transactions of access. Iris

Recognition Technology is the ideal solution in any environment whether you have one door

to protect or one hundred. The unique processing capabilities of the Knowhow server mean

that Iris Recognition is the ONLY technology capable of operating efficiently in situations

where 1000's or even millions of persons must be enrolled. The variability of persons is, after

all, the heart and soul of iris recognition.

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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.

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References

[1] Richard P. Wildes, Member, IEEE, “Iris Recognition: An Emerging Biometric

Technology”, Proceedings Of The IEEE, Vol. 85, No. 9, September 1997

[2] John Daugman, PhD, OBE University of Cambridge, “How Iris Recognition Works", The

Computer Laboratory, Cambridge CB2 3QG, U.K.www.CL.cam.ac.uk/users/jgd1000/

[3] M. Z. Rashad, M. Y. Shams2, O. Nomir, and R. M. El-Awady, “Iris Recognition Based

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[4] Jing Wan, Xiaofu He, Pengfei Shi, ”An Iris Image Quality Assessment Method Based on

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