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Iris Recognition
Post: #1
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This project work entitled - "IRIS RECOGNITION SYSTEM" is aimed at solving the security problem by checking the validity of persons. The main application of this project will be in security is assured on the basis of image identification. This project can be extended by using CCD(Change Coupling Device) camera either active or passive way. We can store the iris images for the identification. This method provides for a much more user friendly experience. We can use the iris as a living password. National border controls the iris as a living passport, telephone call, charging without case, cards or pin nos. secure access to bank case machine accounts.
Post: #2

Presentation by:
Arun Kumar Passi Elec. Engg. (dual deg.)

> Introduction
> Model of a biometric system
> Why Iris
> Image Pre-processing
> Eyelash and Eyelid Removal
> Feature Extraction
> Gabor filter
> Log-Gabor filter
> Performance of the systems
> Time of Operations
> References
> Iris is the annular portion between the dark pupil and white sclera
> It has got a rich texture information which could be exploited for a biometric recognition system

> Its error rate is extremely low ^ Iris a permanent biometric
^ User acceptability is reasonable
> Real time biometric verification
> Less susceptible to spoofing

Claimed Identity
User Interface
Feature Extractor
One template
System Database
True / False
Verification Mode
User Interface
Feature Extractor
System Database
User's Identity / User not identified
Identification Mode
> Images are generally acquired in near infra red illumination
> The distance between the eye and the camera may vary from - cm
> Iris diameter typically should be between - pixels for extracting good texture
> Careful selection of intensity level
^ Image localization
> Detecting the Pupillary circle
> Detecting outer Iris circle
^ Image normalization ^ Image enhancement
Li Ma's approach
> Project image in vertical and horizontal directions
> Minima of two projections will be a rough estimate of pupil center
> Binarize x region centered at that point using adaptive threshold
> Centroid of this binarized region is a better estimate of the pupil center
> Unwrap the Iris region onto a rectangular block of size x

Image Enhancement
Mean of every x block is calculated
Image obtained is resized to x using bi-cubic interpolation
Image Enhancement
> Interpolated image is subtracted from original normalized image
> Image is enhanced through histogram equalization
Eyelash & Eyelid removal
Edge detected image
Eyelash & Eyelid removal

Normalized Image
Normalized Image after noise removal

> Horizontal edge detection is used on the image
> Linear Hough transform is used to fit a line on lower and upper eyelid
> A horizontal line is then drawn intersecting the first line on the iris edge which is closest to the pupil

> Gabor filter
> Log-Gabor filter
> Laplacian of Gaussian filter
> Dyadic wavelet transform
> Mexican hat filter

A Gabor filter is constructed by modulating a sine/cosine wave with a Gaussian
^ Provides the optimum simultaneous localization in both space and frequency
^ The centre frequency of the filter is specified by the frequency of the sine/cosine wave, and the bandwidth of the filter is specified by the width of the Gaussian
^ Daugman makes uses D Gabor filters in order to encode iris pattern data
> The output of Gabor filter is then demodulated to get the phase information which is quantized to four levels for each possible quadrant in complex plane

> The enhanced image is convolved with a bank of Gabor filter at different frequencies and orientations
Source: Li. Ma, Y. Wang, and T. Tan, "Iris recognition based on multi-channel Gabor filtering," in Proc. th Asian Conf. Computer Vision, // vol. I, , pp. -
) Li. Ma, Y. Wang, and T. Tan, "Iris recognition using circular symmetric filters," in Proc. th Int. Conf. Pattern Recognition., vol. II, , pp. -
) Li. Ma, Y. Wang, and T. Tan, "Iris recognition based on multi-channel Gabor filtering," in Proc. th Asian Conf. Computer Vision, vol. I, , pp. -
) Daugman, J. . How iris recognition works. IEEE Trans, CSVT , --
) Daugman, J. The importance of being random: Statistical principles of iris recognition. Pattern Recognition, vol. , num. , pp. -,
) Ma Li, Tan T., Wang Y. and Zhang, D. (): Efficient Iris Recognition by characterizing Key Local Variations, IEEE Trans. Image Processing, vol ,
no., pp. -
) A. Poursaberi, B. N. Araabi, "A Novel Iris Recognition System Using
Morphological Edge Detector and Wavelet Phase Features", ICGST
International Journal on Graphics, Vision and Image Processing,,
Post: #3

Technical Seminar

Presented By
Biometric System

A biometric system refers to automatic

recognition of an individual based on

their physiological and behavioral


Physiological characteristics include

physical characteristics like

face,fingerprints,iris patterns etc.

Behavioral characteristics include

signature, speech patterns etc.

Whatâ„¢s iris?

It is the colored portion (brown or

blue) of the eye that regulates the size

of the pupil.

The coloration and structure of two

irides is genetically linked but the

details of patterns are not.


They have stable and distinctive

features for personal

They are stable with age.
Extremely data rich physical structure

about 400 identifying features.
Its inherent isolation and protection

from the external environment.
The impossibility of surgically

modifying it without unacceptable risk

to vision.
Individuality of Iris
Iris Recognition System
I. Image Acquisition

It deals with capturing of a high

quality image of the iris.
Concerns on the image acquisition rigs

Obtain images with sufficient resolution

and sharpness
Good contrast in the iris pattern with

proper illumination
Well centered without unduly

constraining the operator
Artifacts eliminated as much as possible

I. Image Acquisition
ll. Iris localization
Iris localization is a process to

isolate the iris region from the rest of

the acquired image.

Iris can be approximated by two

circles, One for
iris/sclera boundary and another

for iris/pupil boundary.
How localization is done?
Feature Encoding
Feature encoding was implemented by

convolving the normalized iris pattern

with 1D Log-Gober wavelet.

2D normalized patterns are broken up

into a number of 1D signals

Each row corresponds to a circular ring

on the iris region

The angular direction is taken rather

than the radial one, which corresponds

to columns of normalized pattern.

IV. Pattern Matching

For matching, the Hamming distance was

chosen as a metric for recognition.

The Daugman system computes the

normalized Hamming distance. The hamming

distance between iris code X and Y is

given by:

The result of this computation is then

used as the goodness of match, with

smaller values indicating better

If two patterns are derived from same

iris,the hamming distance between them

will be close to 0 due to high

In order to account for rotational

inconsistencies, one template is shifted

left and right bit-wise and a number of

Hamming distance values are calculated

from successive shifts.

Advantages of the Iris forIdentification

Highly protected, internal organ of the

Patterns apparently stable throughout

Iris patterns possess a high degree of

extremely data-rich physical structure.
Image analysis and encoding time: 1

Search speed: 100,000 Iris Codes per


Disadvantages of the Iris for Identification

Small target (1 cm) to acquire from a

distance (1 m)
Characteristic natural movement of an

eyeball while text is read.
Obscured by eyelashes, lenses,

Deforms non-elastically as pupil changes

Illumination should not be visible or



Current uses:-
government agencies
research laboratories
In prisons
Future uses:-
healthcare industry
Emigration services
sales transaction

Based on the study we can conclude

that iris recognition is one of the

reliable and accurate biometric

Post: #4
We come across many situations in our day-to-day life where personal identification becomes very important. At such times we are reminded of passwords, smart cards or any such identification tokens. But all these have something in common; they can all be easily faked. All these identification methods we know have shortcomings as they are things we possess or know. So why not go for some unique physiological or behavioral trait present in us which can neither be duplicated or faked in any manner? This is where biometrics comes into picture. 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), but also the potential to be an obstacle or excluder if improperly configured or installed without consultation and guidance from disabled persons. 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.
Humans have traditionally identified each other by their appearance, by the sound and content of their speech, and by context. If the other person is neither visible nor audible, e.g. when receiving their email, we either simply accept their asserted identity, allow it to establish itself by shared knowledge and context, or rely on special secret knowledge such as encryption keys. Identification amongst strangers in official interactions, such as immigration passport control or financial transactions, has traditionally relied upon special possessions (documents such as passports and identity cards), or secrets (e.g. passwords).
Post: #5
The term "biometrics" is derived from the Greek words bio (life) and metric (to measure). Biometrics is the science of measuring and statistically analyzing biological data. In information technology, biometrics refers to the use of a person’s biological characteristics for personal identification and authentication. Fingerprint, iris-scan, retinal-scan, voiceprint, signature, handprint and facial features are some of the most common types of human biometrics.
Iris recognition is a method of biometric authentication that uses pattern recognition techniques based on high-resolution images of the irises of an individual's eyes. Iris recognition uses camera technology, with subtle IR illumination reducing specular reflection from the convex cornea, to create images of the detail-rich, intricate structures of the iris. Converted into digital templates, these images provide mathematical representations of the iris that yield unambiguous positive identification of an individual.

2.3.1 Bertillonage 4
2.3.2 Fingerprint Recognition 4
2.3.3 Face Recognition 5
2.3.4 Voice Recognition 6
2.3.5 Iris Recognition 8
2.3.6 Hand Geometry 8
2.3.7 Hand Vascular Pattern Identification 9
2.3.8 Retina Recognition 9
2.3.9 Signature Recognition 9
2.3.10 DNA Recognition 10
3.2.1 Daugman's Algorithm 14
3.2.2 Optimized Daugman's Algorithm 15

First of all, I am grateful to God Almighty, for helping me to do a seminars on this topic. Without His blessings, I would not have been able to complete this seminars.
I hereby express my gratitude to my guides Vinod Chandra S.S. and John Prakash Joseph, Lecturers of Department of Computer applications, College of Engineering Trivandrum, for their valuable guidance, constant encouragement and creative suggestions during the course of this project work, and also in preparing this report. I also express my thanks to Prof. Reji John, Head of the Department, Department of Computer applications, College of Engineering Trivandrum for all necessary help extended by her in the fulfillment of this project work. I am also grateful to my family, all my friends and classmates for their help and support during the preparation and presentation of this paper.
Anjumol K Prasad

Imagine how convenient it would be to activate the security alarm at your home with the touch of a finger, or to enter your home by just placing your hand on the door handle. How would you like to walk up to a nearby ATM which will scan your iris so you can withdraw money without ever inserting a card or entering a PIN. You will basically be able to gain access to everything you are authorized to, by presenting yourself as your identity.
This scenario might not be as far off as we might expect. In the near future, we may no longer use passwords and PIN numbers to authenticate ourselves. These methods have proven to be in secure and unsafe time and time again. Technology has introduced a much smarter solution to us: Biometrics.
Biometric authentication will help in enhancing the security infrastructure against some of these threats. After all, physical characteristics are not something that can be lost, forgotten or passed from one person to another. They are extremely hard to forge and a would-be criminal would think twice before committing a crime involving biometrics.

The four basic elements of a typical biometric system are: sensing, processing, storage and interface to an existing infrastructure.

Biometrics is automated methods of recognizing a person based on a physiological or behavioural characteristic. The word biometrics means Biological Measurements. Therefore in this way we can use computers to recognize persons.
Physiological characteristics means Fingerprints, Retinal and Iris Patterns, Hand and Finger Geometry, Facial recognition etc.
Behavioral characteristics mean Voice Patterns, Signature etc.
There are different biometric solutions. Some of them are Finger Print Recognition, Iris Pattern recognition, Facial Recognition; Voice Pattern Recognition, Hand and Finger Geometry etc. In all these biometric solutions the details about the physiological/behavioral characteristics are entered into a database. When the user uses the system the characteristics required for the system are scanned and a template is formed. It is checked whether there exists a match for this template with any of the records already stored in the database. If a match is found, the user is allowed access. Otherwise the user is denied access.
Each biometric solution can be used in two different modes.
In Identification mode, where the biometric system identifies a person from the entire enrolled population by searching a database for a match.
In Verification mode, where the biometric system authenticates a person's claimed identity from his/her previously enrolled pattern.

The sensing element, or the input interface element, is the hardware core of a biometrics system and converts human biological data into digital form. This could be a complimentary metal oxide semiconductor (CMOS) imager or a charge coupled device (CCD) in the case of face recognition , handprint recognition or iris/retinal recognition systems; a CMOS or optical sensor in the case of fingerprint systems; or a microphone in the case of voice recognition systems.
The following are notes on the biometric sensing.
Finger Print Recognition.

Iris Recognition.

Facial Recognition.
The validity of a biometric system cannot be measured accurately, and can only be enumerated on the occurrence of errors like the chance of accepting an intruder i.e. the False Accept Rate (FAR) and conversely the probability of rejecting a genuine individual i.e. False Reject Rate (FRR) which could turn out to be detrimental to any system.

2.3.1 Bertillonage,
The first type of biometrics came into form in 1890, created by an anthropologist named Alphonse Bertillon. He based his system on the claim that measurement of adult bones does not change after the age of 20. The method consisted of identifying people by taking various body measurements like a person’s height, arm length, length and breadth of the head, the length of different fingers, the length of forearms, etc. using calipers. However, the methodology was unreliable as non-unique measurements allowed multiple people to have same results, decreasing the accuracy and hence is no longer used.
2.3.2 Fingerprint Recognition
It involves taking an image of a person's fingertips and records its characteristics like whorls, arches, and loops along with the patterns of ridges, furrows, and minutiae. Fingerprint matching can be achieved in three ways
Minutae based matching stores minutiae as a set of points in a plane and the points are matched in the template and the input minutiae.
Correlation based matching superimposes two fingerprint images and correlation between corresponding pixels is computed.
Ridge feature based matching is an advanced method that captures ridges, as minutiae capturing are difficult in low quality fingerprint images.
To capture the fingerprints, current techniques employ optical sensors that use a CCD or CMOS image sensor; solid state sensors that work on the transducer technology using capacitive, thermal, electric field or piezoelectric sensors; or ultrasound sensors that works on echography in which the sensor sends acoustic signals through the transmitter toward the finger and captures the echo signals with the receiver.
Fingerprint scanning is very stable and reliable. It secures entry devices for building door locks and computer network accesses are becoming more common. Recently a small number of banks have begun using fingerprint readers for authorization at ATMs.

2.3.3 Face recognition
This technique records face images through a digital video camera and analyses facial characteristics like the distance between eyes, nose, mouth, and jaw edges. These measurements are broken into facial planes and retained in a database, further used for comparison. Face recognition can be done in two ways:
• Face appearance employs Fourier transformation of the face image into its fundamental frequencies and formation of eigenfaces, consisting of eigen vectors of the covariance matrix of a set of training images. The distinctiveness of the face is captured without being oversensitive to noise such as lighting variations.
• Face geometry models a human face created in terms of particular facial features like eyes, mouth, etc. and layout of geometry of these features is computed. Face recognition is then a matter of matching constellations.
Another face identification technology, Facial thermograms, uses infrared heat scans to identify facial characteristics. This non-intrusive technique is light-independent and not vulnerable to disguises. Even plastic surgery, cannot hinder the technique. This technique delivers enhanced accuracy, speed and reliability with minimal storage requirements. To prevent a fake face or mold from faking out the system, many systems now require the user to smile, blink, or otherwise move in a way that is human before verifying. This technique is gaining support as a potential tool for averting terrorism, law enforcement areas and also in networks and automated bank tellers.
2.3.4 Voice Recognition
It combines physiological and behavioral factors to produce speech patterns that can be captured by speech processing technology. Inherent properties of the speaker like fundamental frequency, nasal tone, cadence, inflection, etc. are used for speech authentication.
Voice recognition techniques can be divided into categories depending on the type of authentication domain.
• Fixed text method is a technique where the speaker is required to say a predetermined word that is recorded during registration on the system.
• In the text dependent method the system prompts the user to say a specific word or phrase, which is then computed on the basis of the user’s fundamental voice pattern.
• The text independent method is an advanced technique where the user need not articulate any specific word or phrase. The matching is done by the system on the basis of the fundamental voice patterns irrespective of the language and the text used.
• Conversational technique verifies identity of the speaker by inquiring about the knowledge that is secret or unlikely to be known or guessed by a sham.
This interactive authentication protocol is more accurate as the FAR are claimed to be below 10-12.

Illustrates the differences in the models for two speakers saying the same vowel.
Figure 1.3
The vocal-tract is represented in a parametric form as the transfer function H(z). Ideally, the transfer function should contain poles as well as zeros. However, if only the voiced regions of speech are used then an all-pole model for H(z) is sufficient. Furthermore, linear prediction analysis can be used to efficiently estimate the parameters of an all-pole model. Finally, it can also be noted that the all-pole model is the minimum-phase part of the true model and has an identical magnitude spectra, which contains the bulk of the speaker-dependent information.
This technique is inexpensive but is sensitive to background noise and it can be duplicated. Also, it is not always reliable as voice is subject to change during bouts of illness, hoarseness, or other common throat problems. Applications of this technique include voice-controlled computer system, telephone banking, m-commerce and audio and video indexing.
2.3.5 Iris recognition
It analyzes features like rings, furrows, and freckles existing in the colored tissue surrounding the pupil. The scans use a regular video camera and works through glasses and contact lenses. The image of the iris can be directly taken by making the user position his eye within the field of a single narrow-angle camera. This is done by observing a visual feedback via a mirror. The isolated iris pattern obtained is then demodulated to extract its phase information.
Iris image acquisition can be done in two ways:
• Daugman System that uses an LED based point light source in conjuction with a standard video camera. The system captures images with the iris diameter typically between 100-200 pixels from a distance of 15-46 cm using 330mm lens.
• Wildes System in comparison results in an illumination rig that is more complex. The system images the iris with approximately 256 pixels across the diameter from 20cm using an 80mm lens.
Iris recognition was piloted in Saudi Arabia as a method of keeping track of the millions making Haj. Also it is used a Berkshire County jail for prisoner identification and Frankfurt airport for passenger registration.

2.3.6 Hand geometry
As the name suggests, involves the measurement and analysis of the human hand. Features like length and width of the fingers, aspect ratio of the palm or fingers, width of the palm, thickness of the palm, etc are computed. The user places the palm on a metal surface, which has guidance pegs on it to properly align the palm, so that the device can read the hand attributes.
The basic procedure involves capturing top and side views of the hand using a single camera by judicious placement of a single 45° mirror. To enroll a person in a database, two snapshots of the hand are taken and the average of resulting feature vectors is computed and stored.
Hand Geometry is employed at locations like the Colombian legislatures, San Francisco International Airport, day care centers, a sperm bank, welfare agencies, hospitals, and immigration facilities.
2.3.7 Hand Vascular Pattern Identification
It uses a non-harmful near infrared light to produce an image of one's vein pattern in their face, wrist, or hand, as veins are relatively stable through one's life. It is a non-invasive, computerized comparison of shape and size of subcutaneous blood vessel structures in the back of a hand. The vein "tree" pattern, picked up by a video camera, is sufficiently idiosyncratic to function as a personal code that is extremely difficult to duplicate or discover. The sensor requires no physical contact, providing excellent convenience and no performance degradation even with scars or hand contamination. Verification speed of the system is fast (0.4 sec/person) and the False Acceptance Rate is FAR) and False Rejection Rate (FRR) are extremely low at 0.0001 % and 0.1% respectively. Though minimally used at the moment, vascular pattern scanners can be found in testing at major military installations and is being considered by some established companies in the security industry and multi-outlet retailers.
2.3.8 Retina Recognition
This technology uses infrared scanning and compares images of the blood vessels in the back of the eye, the choroidal vasculature. The eye’s inherent isolation and protection from the external environment as an internal organ of the body is a benefit. Retina scan is used in high-end security applications like military installations and power plants.
2.3.9 Signature recognition
It is an instance of writer recognition, which has been accepted as irrefutable evidence in courts of laws. The way a person signs his name is known to be a characteristic of that individual. Approach to signature verification is based on features like number of interior contours and number of vertical slope components. Signatures are behavioral biometric that can change with time, influenced by physical and emotional conditions of the signatories.
Furthermore, professional forgers can reproduce signatures to fool an unskilled eye and hence is not the preferred choice.
2.3.10 DNA Recognition
It employs Deoxyribo Nucleic Acid, which is the one-dimensional ultimate unique code for ones individuality, except for the fact that identical twins have identical DNA patterns. [2] However, it is currently used mostly used in the context of forensic applications. The basis of DNA identification is the comparison of alleles of DNA sequences found at loci in nuclear genetic material.

3. Iris Recognition Technology
The area of human eye where the pigmented or the coloured circle, usually brown or blue, rings the dark pupil of the eye is called the Iris. The human iris begins to form in the third month of gestation and the structure is complete by the eight month, even though the color and pigmentation continue to build through the first year of birth. After that, the structure of the iris remains stable throughout a person’s life, except for direct physical damage or changes caused by eye surgery. The iris hence parallels the fingerprint in uniqueness but enjoys a further advantage that it is an internal organ and less susceptible to damages over a person’s lifetime. It is composed of several layers which gives it its unique appearance. This uniqueness is visually apparent when looking at its rich and small details seen in high resolution camera images under proper focus and illumination. The iris is the ring-shape structure that encircles the pupil, the dark centered portion of the eye, and stretches radially to the sclera, the white portion of the eye see it shares high-contrast boundaries with the pupil but less-contrast boundaries with the sclera.

Figure 1.4

The iris identification system is to automatically recognize the identity of a person from a new image by comparing it to the human iris patterns annotated with identity in a stored database. A general iris recognition system is composed of four steps. Firstly, an image containing the user’s eye is captured by the system. Then, the image is preprocessed to normalize the scale and illumination of the iris and localize the iris in the image. Thirdly, features representing the iris patterns are extracted. Finally, decision is made by means of matching. There are four key parts the iris recognition system: iris image acquisition, preprocessing, feature extraction, and classifier design.
In a world where we will increasingly do business with parties we’ve never met, and might never meet, authentication will become as integral a part of the transaction as the exchange of goods and tender. The robustness of iris recognition makes it ideal for authenticating parties to commercial transactions, to reduce fraud in applications like check-cashing and ATMs, unauthorized activity in applications like treasury management, and in future, to ensure non-repudiation of sales, or to provide Letter of Credit and other authentication services in an electronic commerce environment. Daugman has shown that iris patterns have about 250 degrees of freedom, i.e. the probability of two eyes having the same iris texture is about 1 in 7 billion. Even the 2 irises of an individual are different thereby suggesting that iris textures are independent of the genetic constitution of an individual. Iris recognition has been successfully deployed in many large scale and small scale applications.


Figure 1.5

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-to-noise ratio considering the focus quality of available cameras. The result is 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 eyelashes. In order to reduce the false-reject risk in such cases, additional algorithms are needed to identify the locations of eye lids and eyelashes, and exclude the bits in the resulting code from the comparison operation.
Iris localization is considered the most difficult part in iris identification algorithms because it defines the inner and outer boundaries of iris region used for feature analysis. The main objective here is to remove any non-useful information, namely the pupil segment and the part outside the iris (sclera, eyelids, skin). R. Wildes used Hough transforms to detect the iris contour. Daugman proposed an integro-differential operator to find both the pupil and the iris contour. Daugman’s algorithm is claimed to be the most efficient one. After analyzing The Daugman's iris locating and pointing out the some limitations of this algorithm, this paper proposes optimized Daugman’s algorithms for iris localization.
3.2.1 Daugman's Algorithm:
Daugman's algorithm is based on applying an integro-differential operator to find the iris and pupil contour.

Where X0, Y0, ro : the center and radius of coarse circle (for each of pupil and iris). Gσ® : Gaussia function. Δr : the radius range for searching for. I(X, Y) : the original iris image.
Gσ® is a smoothing function, the smoothed image is then scanned for a circle that has a maximum gradient change, which indicates an edge. The above algorithm is done twice, first to get the iris contour then to get the pupil contour. It worth mentioning here the problem is that the illumination inside the pupil is a perfect circle with very high intensity level (nearly pure white). Therefore, we have a problem of sticking to the illumination as the max gradient circle. So a minimum pupil radius should be set. Another issue here is in determining the pupil boundary the maximum change should occur at the edge between the very dark pupil and the iris, which is relatively darker than the bright spots of the illumination. Hence, while scanning the image one should take care that a very bright spot value could deceive the operator and can result in a maximum gradient. This simply means failure to localize the pupil. The following experimental results have been getting using UPOL database.

3.2.2 Optimized Daugman's Algorithm:
As a solution to this problem, modification to the integro-differential operator is proposed to ignore all circles if any pixel on this circle has a value higher than a certain threshold. This threshold is determined to be 200 for the grayscale image. This ensures that only the bright spots – values usually higher than 245 – will be cancelled.
Another solution we considered is to treat the illumination by truncating pixels higher than a certain threshold – bright spots – to black. But this method failed in many images, this is because when the spot hits the pupil the illumination spreads on the pupil so as we treat the illumination spots it will leave behind a maximum change edges that can not be determined and the operator will consider it the pupil boundary. The sequence of the Algorithm procedure is cleared in the flowchart shown below.
The false acceptance rate for the iris recognition system 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 three percent false rejection rate, whereas iris scanning systems boast rate at the 0% level.

Figure Flowchart of optimized Daugman's localization algorithm operation.

Figure 1.6

Localization result
Figure 1.7

Data base
Number of samples


The proposed algorithm is tested by applying it on UPOL database that includes about 384 images for 128 persons the localization successful percentage was 100%.

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 brought very close to a lens (like looking into a microscope lens).
Some argue that a focused digital photograph with an iris diameter of about 200 pixels contains much more long-term stable information than a fingerprint.
The originally commercially deployed iris recognition algorithm, John Daugman's IrisCode, has an unprecedented false match rate (better than 10−11).
While there are some medical and surgical procedures that can affect the colour and overall shape of the iris, the fine texture remains remarkably stable over many decades. Some iris identifications have succeeded over a period of about 30 years.
3.4 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.

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 papillary 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 fake-iris contact lenses
Analysing the temporal frequency spectrum of the image for the peaks caused by computer displays
Using spectral analysis instead of merely monochromatic cameras 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
A 2004 report by the German Federal Office for Information Security noted that none of the iris-recognition systems commercially available at the time implemented any live-tissue verification technology. Like any pattern-recognition technology, live-tissue verifiers will have their own false-reject probability and will therefore further reduce the overall probability that a legitimate user is accepted by the sensor.

Biometrics is a truly emerging market with great potential for success. Its roots may be in science fiction, but it is part of today’s science and technology fact. In the near future, we will come to rely on biometric technology to protect our property, assets, and the people we love. We will see this technology become a secure and trusted form of authentication with uses varying from controlling access to personal information devices, to securing buildings and enabling eCommerce.
An important point to be noted in constructing a biometric system is that it should be based upon a distinguishable trait. For eg: Law enforcement has used finger prints to identify people. There is a great deal of scientific data supporting the idea that “no fingerprints are alike”. All biometric systems capture data from individuals. Once these dates have been captured by the system, they can be forwarded to any location and put to many different uses which are capable of compromising on an individual's privacy. A good biometric system is one that is of low cost, fast, accurate, and easy to use.


Post: #6
hi sir this Rajnsh how we read iris pattren in the eyes thorough c# .
Post: #7

In today’s information age it is not difficult to collect data about an individual and use that information to exercise control over the individual. Individuals generally do not want others to have personal information about them unless they decide to reveal it. With the rapid development of technology, it is more difficult to maintain the levels of privacy citizens knew in the past. In this context, data security has become an inevitable feature. Conventional methods of identification based on possession of ID cards or exclusive knowledge like social security number or a password are not altogether reliable. ID cards can be almost lost, forged or misplaced: passwords can be forgotten. Such that an unauthorized user may be able to break into an account with little effort. So it is need to ensure denial of access to classified data by unauthorized persons. Biometric technology has now become a viable alternative to traditional identification systems because of its tremendous accuracy and speed. Biometric system automatically verifies or recognizes the identity of a living person based on physiological or behavioral characteristics. Since the persons to be identified should be physically present at the point of identification, biometric techniques gives high security for the sensitive information stored in mainframes or to avoid fraudulent use of ATMs.This paper explores the concept of Iris recognition which is one of the most popular biometric techniques. This technology finds applications in diverse fields.
Biometric dates back to ancient Egyptians who measured people to identify them. Biometric devices have three primary components.
1. Automated mechanism that scans and captures a digital or analog image of a living personal characteristic
2. Compression, processing, storage and comparison of image with a stored data.
3. Interfaces with application systems.
A biometric system can be divided into two stages: the enrolment module and the identification module. The enrolment module is responsible for training the system to identity a given person. During an enrolment stage, a biometric sensor scans the person’s physiognomy to create a digital representation. A feature extractor processes the representation to generate a more compact and expressive representation called a template. For an iris image these include the various visible characteristics of the iris such as contraction, Furrows, pits, rings etc. The template for each user is stored in a biometric system database. The identification module is responsible for recognizing the person. During the identification stage, the biometric sensor captures the characteristics of the person to be identified and converts it into the same digital format as the template. The resulting template is fed to the feature matcher, which compares it against the stored template to determine whether the two templates match.
The identification can be in the form of verification, authenticating a claimed identity or recognition, determining the identity of a person from a database of known persons. In a verification system, when the captured characteristic and the stored template of the claimed identity are the same, the system concludes that the claimed identity is correct. In a recognition system, when the captured characteristic and one of the stored templates are the same, the system identifies the person with matching template.

Biometrics encompasses both physiological and behavioral characteristics. A physiological characteristic is a relatively stable physical feature such as finger print, iris pattern, retina pattern or a Facial feature. A behavioral trait in identification is a person’s signature, keyboard typing pattern or a speech pattern. The degree of interpersonal variation is smaller in a physical characteristic than in a behavioral one. For example, the person’s iris pattern is same always but the signature is influenced by physiological characteristics.
Even though conventional methods of identification are indeed inadequate, the biometric technology is not as pervasive and wide spread as many of us expect it to be. One of the primary reasons is performance. Issues affecting performance include accuracy, cost, integrity etc.
Even if a legitimate biometric characteristic is presented to a biometric system, correct authentication cannot be guaranteed. This could be because of sensor noise, limitations of processing methods, and the variability in both biometric characteristic as well as its presentation.
Cost is tied to accuracy; many applications like logging on to a pc are sensitive to additional cost of including biometric technology.

Iris identification technology is a tremendously accurate biometric. Iris recognition leverages the unique features of the human iris to provide an unmatched identification technology. So accurate are the algorithms used in iris recognition that the entire planet could be enrolled in an iris database with only a small chance of false acceptance or false rejection. The technology addresses the FTE (Failure to Enroll) problems which lessen the effectiveness of other biometrics. Only the iris recognition technology can be used effectively and efficiently in large scale identification implementations. The tremendous accuracy of iris recognition allows it, in many ways, to stand apart from other biometric technologies.
The word IRIS dates from classical times (a rainbow). The iris is a Protective internal organ of the eye. It is easily visible from yards away as a colored disk, behind the clear protective window of the cornea, surrounded by the white tissue of the eve. It is the only internal organ of the body normally visible externally. It is a thin diaphragm stretching across the anterior portion of the eye and supported by lens. This support gives it the shape of a truncated cone in three dimensions. At its base the eye is attached to the eye’s ciliary body. At the opposite end it opens into a pupil. The cornea and the aqueous humor in front of the iris protect it from scratches and dirt, the iris is installed in its own casing. It is a multi layered structure. It has a pigmented layer, which forms a coloring that surrounds the pupil of the eye. One feature of this pupil is that it dilates or contracts in accordance with variation in light intensity.
The human iris begins to form during the third month of gestation. The structures creating its distinctive pattern are completed by the eighth month of gestation hut pigmentation continues in the first years after birth. The layers of the iris have both ectodermic and embryological origin, consisting of: a darkly pigmented epithelium, pupillary dilator and sphincter muscles, heavily vascularized stroma and an anterior layer chromataphores with a genetically determined density of melanin pigment granules. The combined effect is a visible pattern displaying various distinct features such as arching ligaments, crypts, ridges and zigzag collaratte. Iris color is determined mainly by the density of the stroma and its melanin content, with blue irises resulting from an absence of pigment: long wavelengths are penetrates and is absorbed by the pigment epithelium, while shorter wavelengths are reflected and scattered by the stroma. The heritability and ethnographic diversity of iris color have long been studied. But until the present research, little attention had been paid to the achromatic pattern complexity and textural variability of the iris among individuals.
A permanent visible characteristic of an iris is the trabecular mesh work, a tissue which gives the appearance of dividing the iris in a radial fashion. Other visible characteristics include the collagenous tissue of the stroma, ciliary processes, contraction furrows, crypts, rings, a corona and pupillary frill coloration and sometimes freckle. The striated anterior layer covering the trabecular mesh work creates the predominant texture with visible light.

Iris is the focus of a relatively new means of biometric identification. The iris is called the living password because of its unique, random features. It is always with you and can not be stolen or faked. The iris of each eye is absolutely unique. The probability that any two irises could be alike is one in 10 to 78th power — the entire human population of the earth is roughly 5.8 billion. So no two irises are alike in their details, even among identical twins. Even the left and right irises of a single person seem to be highly distinct. Every iris has a highly detailed and unique texture that remains stable over decades of life. Because of the texture, physiological nature and random generation of an iris artificial duplication is virtually impossible.
The properties of the iris that enhance its suitability for use in high confidence identification system are those following.
1. Extremely data rich physical structure about 400 identifying features
2. Genetic independence no two eyes are the same.
3. Stability over time.
4. Its inherent isolation and protection from the external environment.
5. The impossibility of surgically modifying it without unacceptable risk to vision.
6. Its physiological response to light, which provides one of several natural tests against artifice.
7. The ease of registering its image at some distance forms a subject without physical contact. unobtrusively and perhaps inconspicuously
8. It intrinsic polar geometry which imparts a natural co-ordinate system and an origin of co-ordinates
9. The high levels of randomness in it pattern inter subject variability spanning 244 degrees of freedom - and an entropy of 32 bits square million of iris tissue.
The idea of using patterns for personal identification was originally proposed in 1936 by ophthalmologist Frank Burch. By the 1980’s the idea had appeared in James Bond films, but it still remained science fiction and conjecture. In 1987, two other ophthalmologists Aram Safir and Leonard Flom patented this idea and in 1987 they asked John Daugman to try to create actual algorithms for this iris recognition. These algorithms which Daugman patented in 1994 are the basis for all current iris recognition systems and products.
Daugman algorithms are owned by Iridian technologies and the process is licensed to several other Companies who serve as System integrators and developers of special platforms exploiting iris recognition in recent years several products have been developed for acquiring its images over a range of distances and in a variety of applications. One active imaging system developed in 1996 by licensee Sensar deployed special cameras in bank ATM to capture IRIS images at a distance of up to 1 meter. This active imaging system was installed in cash machines both by NCR Corps and by Diebold Corp in successful public trials in several countries during I997 to 1999. a new and smaller imaging device is the low cost “Panasonic Authenticam” digital camera for handheld, desktop, e-commerce and other information security applications. Ticket less air travel, check-in and security procedures based on iris recognition kiosks in airports have been developed by eye ticket. Companies in several, countries are now using Daughman’s algorithms in a variety of products.
The design and implementation of a system for automated iris recognition can be subdivided in to three:
1. image acquisition
2. iris localization and
3. Pattern matching
Post: #8
Iris Recognition

 Iris recognition is a method of biometric authentication that uses pattern-recognition techniques based on high-resolution images of the irises of an individual's eyes.
 Iris is a muscle within the eye that regulates the size of pupil, controlling the amount of light that controls the eye.
 In this figure you can see the iris.
 In 1936,opthamologist Frank Burch proposed the concept of using iris patterns as a method to recognize an individual.
 In 1985, Dr. Leonard Flom and Dr. Aran Safir, two ophthalmologists, proposed a concept that no two irises are alike and were awarded a patent for this in the year 1987.
 It was not until 1995, that the first commercial product was available.
 Recording of Identities
 Creating Generation
 The iris is scanned and data is stored as below.
 Code Generation
 IrisCode Formation
 Measure of Performance
 Authentification of Iris ( password )
 Iris Scanners
 Here are some key features of "Iris Recognition System":
 Highly optimized code: the execution time reduced of 94%, more than 16 times faster than original code.
 Matching module
 Optimized memory allocation
 Iris recognition
 Interactive and intuitive GUI
 C code included
 National border controls: the iris as a living passport
 Computer login: the iris as a living password
 Secure access to bank accounts at cash machines
 Driving licenses; other personal certificates
 Anti-terrorism (e.g. security screening at airports)
 Secure financial transactions (electronic commerce, banking)
 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) that 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.
 The originally commercially deployed iris-recognition algorithm, John Daugman's Iris Code, has an unprecedented false match rate.
 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.
 Iris recognition is susceptible to poor image quality, with associated failure to enroll rates.
 Having only become automated and available within the past decade, the iris recognition concept and industry are relatively new so a need for continued research and testing remains.
 Though the determination and commitment of industry, government evaluations, and organized standard bodies, growth and progress will continue, raising the bar for iris recognition technology.
Post: #9
Presented By
Swati G. Manikpure

 What is Biometrics?
 Why Biometrics is used?
 How Biometrics is today?
Broad Classification
 Why Iris Recognition?
 The only INTERNAL organ EXTERNALLY visible
 Stable for a life time
 Eye injuries or operations cannot change the Iris pattern what so ever
Iris Recognition systems
 The iris-scan process begins with a photograph. A specialized camera, typically very close to the subject, not more than three feet, uses an infrared imager to illuminate the eye and capture a very high-resolution photograph. This process takes 1 to 2 seconds.
Creating an Iris code
 The picture of eye first is processed by software that localizes the inner and outer boundaries of the iris.
 And it is encoded by image-processing technologies.
Iris recognition
 In less than few seconds, even on a database of millions of records, the iris code template generated from a live image is compared to previously enrolled ones to see if it matches to any of them.
 Example of iris recognition system
 Typical iris system configuration
Typical iris system configuration for taking a picture
 An iris recognition camera takes a black and white picture from 5 to 24 inches away.
 The camera uses non-invasive, near-infrared illumination that is barely visible and very safe.
Techniques used
 Iris Localization
 Iris Normalization
 Image Enhancement
Iris Localization
 Purpose: 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 the border between the sclera and the iris and outside the pupil.
 Desired characteristics of iris localization:
◦ Sensitive to a wide range of edge contrast
◦ Robust to irregular borders
◦ Capable of dealing with variable occlusions
 The Daugman system fits the circular contours via gradient ascent on the parameters so as to maximize
Iris Normalization
 The size of the pupil may change due to the variation of the illumination and the associated elastic deformations in the iris
 Texture may interface with the results of pattern matching.
 For the purpose of accurate texture analysis, it is necessary to compensate this deformation.
Iris Enhancement
 Pattern Matching
• Bringing the recent iris pattern into alignment with a candidate data base entry.
• Evaluating the goodness of match between the newly acquired and data base representations.
• Deciding if the newly acquired data and the data base entry were derived from the same iris based on the goodness of match.
 Can we fool this technique?
 present a high resolution picture of a person.. just wear a mask!
 wear a specs or colored contact lens.. what if I have naturally dark black eyes ?
 Pupil dilation or Disorders or diabetes or natural aging ?
 Combines computer vision, pattern recognition , statistical inference, and optics
 Purpose - real-time, high confidence recognition of a person’s identity
 The iris technology is expanding into the most reliable biometrics feature---data rich of physical structure, accurate, secure, stable, and safe
 The only disadvantage was the designing and manufacturing cost
Post: #10
Submitted By-
Miss. Almas Kanjiyani

A method based for rapid visual recognition of personal identity is described on the failure of statistical test of independence. The most unique feature visible in a person’s face is the detailed texture of each eye’s iris. Iris based identity recognition is one of the most important parts of biometrics.
The problem of the personal identification has become a great matter in today’s world. Biometrics, which means biological features based identity recognition, has provided a convenient and reliable solution to this problem. This recognition technology is relatively new with many significant advantages, such as speed, accuracy, hardware, simplicity and applicability.
An iris has a mesh like texture to it, with numerous overlays and patterns. Basically, iris recognition system comprises of four main modules: Image Acquisition, Preprocessing, Feature Extraction and Pattern Matching. Firstly an image containing the users eye is captured by the system. Then the image is preprocessed. Thirdly, features representing the iris patterns are extracted. Finally, decision is made by means of matching.
To show that, although iris recognition is still in its final research and development stage throughout the world, it has many possible applications, some of which are listed below:
1.Secure access to bank case machine accounts.
2.Ticketless air travel.
3. Driving license, and other personal certificates.
4.Internet security, control of access to privileged information
With communications among people constantly increasing nowadays, how to recognize people’s identity has become an essential problem. The traditional methods such as keys, certificates, passwords, etc, can hardly meet the requirements of identity recognition in the modern society. Biometrics, which means biological features based identity recognition, has provided a convenient and reliable solution to this problem.
Iris based identity recognition is one of the most important parts of biometrics due to its various advantages, such as preciseness and no need of direct contact with the testees. According to some comparative research, the error rate of iris recognition is the lowest one among all the biometrics approaches till date.
Many users are skeptical of the use of such a technology due to wearers of eyeglasses, contact lenses, or sunglasses. However, the recognition system is able to perform right through glasses or lenses since they do not interfere with the process. There is no need for a customer to take off their glasses in order to be identified quickly and accurately.
Iris recognition technology was designed to be less intrusive than retina scans, which often require infrared rays or bight light to get an accurate reading. Scientists also say a person’s retina can change with age, while an iris remains intact. And no two blueprints are mathematically alike, even between identical twins and triplets. During the course of examining large number of files, anatomists and ophthalmologists have noted that the detained pattern of an iris, even the left and the right iris of a single person, seem to be highly distinctive.
In addition recent medical advances such as refractive surgery, cataract surgery and cornea transplants do not change iris’ characteristics. In fact, it is impossible to modify the iris without risking blindness. And even a blind person can participate. As long as a sightless eye has an iris, that eye can be identified by iris recognition.
An iris has a mesh-like texture to it, with numerous overlays and patterns. The iris is located behind the cornea of the eye, but in front of the lens. Its only physiological purpose is to control the amount of light that enters the eye through the pupil, but its construction from elastic connective tissue gives it a complex, fibrillous pattern.
Research shows the iris is one of the most unique data rich physical structures on the human body. An iris has 256 independent measurable characteristics, or degrees of freedom, nearly six times as many as a finger print. Thus, the probability of two irises producing the same code is approximately 1 in 1078. , With the population of the earth being approximately 1010 people.
Thus, the performance of iris recognition is at a much higher level of scientific certainty and has many greater capabilities then any other form of Human recognition, including finger prints, Facial or voice recognition, and retinal recognition. This recognition technology is relatively new with many significant advantages, such as speed, accuracy, hardware, simplicity, and applicability.
Accurately identifying individuals is a major concern for governmental agencies, police department, medical institutions, Banking and legal institutions, and corporation, to name just a few. The importance lies in the necessity for the control of fraud, efficiency in administration, and benefits to users of various systems.
Iris has stable and distinctive features for personal identification. That is because every iris has fine and unique patterns and does not change over time since two or three years after the birth, so it might be called as a kind of optical finger print.
The iris identification program may be divided into four main functional blocks:
Firstly, an image containing the user’s eye is captured by the system. Then, the images preprocessed to normalize the scale and illumination of the iris and localize the iris pattern are extracted. Finally decision is made by the means of matching.
An image surrounding human eye region is obtained at a distance from a CCD camera without any physical contact to the device. Figure shows the device configuration for acquiring human eye images. To acquire more clear images through a CCD camera and minimize the effect of the reflected lights caused by the surrounding illumination, we arrange two halogen lamps as the surrounding lights, as the figure illustrates. The size of the image acquired under this circumstance is 320 x 240.
The acquired image always contains not only the “useful” parts (IRIS) but also some “relevant” parts (e.g. eyelid, pupil). Under some conditions, the brightness is not uniformly distributed. In addition, different eye-to-camera distance may result in different image sizes of the same eye. For the purpose of analysis, the original image needs to be processed. The processing is composed of two steps:
1. Iris Localization.
2. Edge Detection.
• Iris Localization:
In this stage, we should determine an iris part of the image by localizing the position of the image derived from inside the limbus (outer boundary) and outside the pupil (inner boundary), and finally convert the iris part into a suitable representation. Because there is some obvious difference in the intensity around each boundary, an edge detection method is easily applied to acquire the edge information.
• Edge Detection:
It is used to find complex object boundaries by marking potential edge points corresponding to places in an image where rapid change in brightness occurs. After edge points have been marked, they can be merged to for lines. Edge detection operators are based on idea that edge information in an image is found by looking at the relationship of a pixel with its neighbors. In other words, edge is defined by discontinuity in gray values. An edge separates two distinct objects.
The approach used for the feature extraction is to extract the relevant pixel values from the iris image using the Fast Fourier Transform (FFT). Before seeing something about FFT, let us see what Fourier Transform is:
In many signal-processing applications, the distinguishing features of signals are mostly interpreted in the frequency domain. The main analytic tool for the frequency domain properties of discrete time signals and the frequency domain behavior of discrete time system is the Fourier Transform. We extract these features by extracting a buffer of 256 pixel values. Once this buffer is extracted, we find the FFT (Fast Fourier Transform) of the extracted buffer. The modules value of FFT is stored as an array in the database and this is used for matching a test image with one’s available in database. Also the phase is calculated and is stored in other array.
The pattern matching process may be decomposed into four parts:
1. Bringing the newly acquired iris pattern into spatial alignment with a candidate database entry.
2. Choosing a representation of the aligned iris pattern that makes their distinctive pattern apparent.
3. Evaluating the goodness of a match between the newly acquired and database representation.
4. Deciding if the newly acquired data and the database entry were derived from the same iris based on the goodness of the match.
The comparison between a new “test” iris code and database of existing codes is performed in the following manner:
The exclusive-OR function is taken for each difference between the two codes. Bit #1 from the reference iriscode code record, bit #2 from the presented iriscode record is compared to bit #2 from the reference iriscode record, and so on. If two bits are alike, the system assigns a value of zero to that pair comparison. If the two bits are different, the system assigns a value of one to that pair comparison. After all pairs are compared, the total number of bit-pair divides the number of disagreeing bit-pairs. This value is termed as “Hamming Distance”. A Hamming Distance of .10 means that two iriscode records differed by 10%.
At Hamming Distance (.342), the probability of a False Reject is approximately the same as the probability of a False Accept. When two iris code differ I more than 34.2% of their bits, they are considered to be different, if fewer than 34.2% of their bits difference they are considered to be from identical irises.
 Using Euclidian Distance:
Euclidian Distance =  (Ai -Bi) 2
Where, Ai = Absolute FFT element of test image.
Bi = Absolute FFT elements of image from database.
The minimum Euclidian distance corresponds to the image in the database, which matches most closely, with the image. A very high threshold level for Euclidian distance is set so as to accept the image in database as the correctly matched image with high authencity. The Euclidian distance above which the image is declared as rejected is said to be 0.005, whereas the typical Euclidian distance for other images are of the order of 103 and 104
 Secure accesses to bank cash machine accounts:
The banks of United, Diebold and Sensar have applied it. After enrolling once (a “30 ”second process), the customers need only approach the ATM, follow the instruction to look at the camera, and be recognized within 2-4 seconds. The ultimate aim is to provide safe and secure transactions.
 Ticket less, document-free air travel:
Passengers and airline employees will store digital images of their irises on a database. After the image of your iris is on the file, a video camera will be able to instantly verify your identity and clear you to board the aircraft.
 Computer login: the iris an living password.
 National border controls: the iris as a living passport.
 Premises access control (homes, office, laboratory).
 Credit card authentication.
 Secure financial transactions.
 Internet security.
 Highly protected internal organ of the eye.
 Externally visible patterns imaged from a distance.
 Iris patterns possesses a high degree of randomness
 Variability: 266 degrees-of-freedom.
 Limited genetic penetrance of iris patterns.
 Patterns apparently stable throughout life.
 Encoding and decision-making are tractable.
 Image analysis and encoding time: 1 second.
 Search speed: 100,000 Iris codes per seconds.
 Small targets (1 cm) to acquire from a distance of 1m.
 Moving target…within another…on yet another.
 Obscured by eyelashes, reflections.
 Partially occluded by eyelids.
Post: #11
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