A Seminar

Report

on

BIOMETRIC AUTHENTICATION

Submitted by:-

Sayani Mondal

Roll No:- 10IT61K01

Department of Information and communication Technology

ABSTRACT

Humans recognize each other according to their various characteristics for ages. We

recognize others by their face when we meet them and by their voice as we speak to them.

Identity verification (authentication) in computer systems has been traditionally based on

something that one has (key, magnetic or chip card) or one knows (PIN, password). Things like

keys or cards, however, tend to get stolen or lost and passwords are often forgotten or

disclosed.

To achieve more reliable verification or identification we should use something that

really characterizes the given person. Biometrics offer automated methods of identity

verification or identification on the principle of measurable physiological or behavioral

characteristics such as a fingerprint or a voice sample. The characteristics are measurable and

unique. These characteristics should not be duplicable, but it is unfortunately often possible to

create a copy that is accepted by the biometric system as a true sample.

In biometric-based authentication, a legitimate user does not need to remember

or carry anything and it is known to be more reliable than traditional authentication schemes.

However, the security of biometric systems can be undermined in a number of

ways. For instance, a biometric template can be replaced by an impostor's template in a system

database or it might be stolen and replayed . Consequently, the impostor could gain

unauthorized access to a place or a system. Moreover, it has been shown that it is possible to

create a physical spoof starting from standard biometric templates. Hence, securing the

biometric templates is vital to maintain security and integrity of biometric systems.

This report actually gives an overview of what is biometric system and a detail overview

of a particular system i.e iris recognition system.

Table of contents

1. INTRODUCTION

1.1 HISTORY AND DEVELOPMENT OF BIOMETRICS

1.2 BASIC STRUCTURE OF A BIOMETRIC SYSTEM

1.3 CLASSIFICATION OF BIOMETRICS

1.4 TYPES OF BIOMETRICS

2. IRIS RECOGNITION

2.1 INTRODUCTION

2.2 ANATOMY OF THE HUMAN IRIS

2.3 STAGES INVOLVED IN IRIS DETECTION

2.3.1 IMAGE ACQUISITION ANS SEGMENTATION

2.3.2 NORMALIZATION

2.3.3 FEATURE ENCODING AND MATCHING

2.4 BIOMETRIC SYSTEM PERFORMANCES

2.5 DECISION ENVIRONMENT

3. ADVANTAGES AND DISADVANTAGES

4. CONCLUSION

1 INTRODUCTION

Biometrics are automated methods of identifying a person or verifying the identity of a person

based on a physiological or behavioral characteristic. Biometric-based authentication is the

automatic identity verification, based on individual physiological or behavioral characteristics,

such as fingerprints, voice, face and iris. Since biometrics is extremely difficult to forge and

cannot be forgotten or stolen, Biometric authentication offers a convenient, accurate,

irreplaceable and high secure alternative for an individual, which makes it has advantages over

traditional cryptography-based authentication schemes. It has become a hot interdisciplinary

topic involving biometric and Cryptography. Biometric data is personal privacy information,

which uniquely and permanently associated with a person and cannot be replaced like

passwords or keys. Once an adversary compromises the biometric data of a user, the data is lost

forever, which may lead to a huge financial loss. Hence, one major concern is how a person’s

biometric data, once collected, can be protected.

1.1 HISTORY AND DEVELOPMENT OF BIOMETRICS

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.

1.2 BASIC STRUCTURE OF A BIOMETRIC SYSTEM

Biometric authentication requires comparing a registered or enrolled biometric sample (biometric

template or identifier) against a newly captured biometric sample (for example, a fingerprint

captured during a login).

During Enrollment, a sample of the biometric trait is captured, processed by a computer, and

stored for later comparison.

Biometric recognition can be used in Identification mode, where the biometric system identifies

a person from the entire enrolled population by searching a database for a match based solely on

the biometric. For example, an entire database can be searched to verify a person has not applied

for entitlement benefits under two different names. This is sometimes called “one-to-many”

matching.

A system can also be used in Verification mode, where the biometric system authenticates a

person’s claimed identity from their previously enrolled pattern. This is also called “one-to-one”

matching. In most computer access or network access environments, verification mode would be

used. A user enters an account, user name, or inserts a token such as a smart card, but instead of

entering a password, a simple touch with a finger or a glance at a camera is enough to authenticate

the user.

1.3 Classification of Biometrics

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.

1.4 TYPES OF BIOMETRICS

Fingerprints: The patterns of friction ridges and valleys on an individual's fingertips are unique to

that individual. For decades, law enforcement has been classifying and determining identity by

matching key points of ridge endings and bifurcations. Fingerprints are unique for each finger of

a person including identical twins. One of the most commercially available biometric

technologies, fingerprint recognition devices for desktop and laptop access are now widely

available from many different vendors at a low cost. With these devices, users no longer need to

type passwords – instead, only a touch provides instant access.

Face Recognition: The identification of a person by their facial image can be done in a number of

different ways such as by capturing an image of the face in the visible spectrum using an

inexpensive camera or by using the infrared patterns of facial heat emission. Facial recognition

in visible light typically model key features from the central portion of a facial image. Using a

wide assortment of cameras, the visible light systems extract features from the captured

image(s) that do not change over time while avoiding superficial features such as facial

expressions or hair.

Speaker Recognition:. Speaker recognition uses the acoustic features of speech that have been

found to differ between individuals. These acoustic patterns reflect both anatomy and learned

behavioral patterns . This incorporation of learned patterns into the voice templates has earned

speaker recognition its classification as a "behavioral biometric." Speaker recognition systems

employ three styles of spoken input: text-dependent, text-prompted and text independent.

Most speaker verification applications use text-dependent input, which involves selection and

enrollment of one or more voice passwords. Text-prompted input is used whenever there is

concern of imposters. The various technologies used to process and store voiceprints include

hidden Markov models, pattern matching algorithms, neural networks, matrix representation

and decision trees.

Iris Recognition: This recognition method uses the iris of the eye which is the colored area that

surrounds the pupil. Iris patterns are thought unique. The iris patterns are obtained through a

video-based image acquisition system. Iris scanning devices have been used in personal

authentication applications for several years. Systems based on iris recognition have

substantially decreased in price and this trend is expected to continue. The technology works

well in both verification and identification modes.

Hand and Finger Geometry: To achieve personal authentication, a system may measure either

physical characteristics of the fingers or the hands. These include length, width, thickness and

surface area of the hand. One interesting characteristic is that some systems require a small

biometric sample. It can frequently be found in physical access control in commercial and

residential applications, in time and attendance systems and in general personal authentication

applications.

Signature Verification: This technology uses the dynamic analysis of a signature to authenticate

a person. The technology is based on measuring speed, pressure and angle used by the person

when a signature is produced. One focus for this technology has been e-business applications

and other applications where signature is an accepted method of personal authentication.

2. IRIS RECOGNITION

2.1 INTRODUCTION

Iris recognition systems, in particular, are gaining interest because the iris’s rich texture offers a

strong biometric clue for recognizing individuals. Located just behind the cornea and in front of

the lens, the iris uses the dilator and sphincter muscles that govern pupil size to control the

amount of light that enters the eye. Near-infrared (NIR) images of the iris’s anterior surface

exhibit complex patterns that computer systems can use to recognize individuals. Because NIR

lighting can penetrate the iris’s surface, it can reveal the intricate texture details that are present

even in dark-colored irises. The iris’s textural complexity and its variation across eyes have led

scientists to postulate that the iris is unique across individuals. Further, the iris is the only

internal organ readily visible from the outside. Thus, unlike fingerprints or palm prints,

environmental effects cannot easily alter its pattern. An iris recognition system uses pattern

matching to compare two iris images and generate a match score that reflects their degree of

similarity or dissimilarity.

2.2 Anatomy of the Human Iris

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

of the human eye.The iris is perforated close to its center 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 colour of the iris. The externally visible surface of the multi-layered

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

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

Formation of the iris begins during the third month of embryonic life .

The unique pattern on the surface of the iris is formed during the first year of

life, and pigmentation of the stroma takes place for the first few years. Formation of the unique

patterns of the iris is random and not related to any genetic factors . The only characteristic that

is dependent on genetics is the pigmentation of the iris, which determines its colour. Due to the

epigenetic nature of iris patterns, the two eyes of an individual contain completely independent

iris patterns, and identical twins possess uncorrelated iris patterns.

2.3 STAGES INVOLVED IN IRIS DETECTION

It includes Three Main Stages

2.3.1) Image Acquisition and Segmentation

2.3.2) Image Normalization

2.3.3)Feature Coding and Matching

2.3.1IMAGE ACQUISITION AND SEGMENTATION

IMAGE ACQUISITION

One of the major challenges of automated iris recognition is to capture a high-quality image of

the iris while remaining non invasive to the human operator.

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

SEGMENTATION

The first stage of iris recognition is to isolate the actual iris region in a digital eye image. The iris

region can be approximated by two circles, one for the iris/sclera boundary and another, interior

to the first, for the iris/pupil boundary. The eyelids and eyelashes normally occlude the upper

and lower parts of the iris region. Also, specular reflections can occur within the iris region

corrupting the iris pattern. A technique is required to isolate and exclude these artifacts as well

as locating the circular iris region.

This can be done by using the following techniques:-

• Hough Transform

• Daugman Integro- Differential operator

Hough Transform:

The Hough transform is a standard computer vision algorithm that can be used to determine the

parameters of simple geometric objects, such as lines and circles, present in an image. The

circular Hough transform can be employed to deduce the radius and centre coordinates of the

pupil and iris regions. Firstly, an edge map is generated by calculating the first derivatives of

intensity values in an eye image and then thresholding the result. From the edge map, votes are

cast in Hough space for the parameters of circles passing through each edge point. These

parameters are the centre coordinates xc and yc, and the radius r, which are able to define any

circle according to the equation

A maximum point in the Hough space will correspond to the radius and centre coordinates of

the circle best defined by the edge points. Wildes et al. make use of the parabolic Hough

transform to detect the eyelids, approximating the upper and lower eyelids with parabolic arcs,

which are represented as;

In performing the preceding edge detection step, Wildes et al. bias the derivatives in the

horizontal direction for detecting the eyelids, and in the vertical direction for detecting the outer

circular boundary of the iris, this is illustrated in Figure shown below. The motivation for this is

that the eyelids are usually horizontally aligned, and also the eyelid edge map will corrupt the

circular iris boundary edge map if using all gradient data.

Taking only the vertical gradients for locating the iris boundary will reduce influence of the

eyelids when performing circular Hough transform, and not all of the edge pixels defining the

circle are required for successful localization. Not only does this make circle localization more

accurate, it also makes it more efficient, since there are less edge points to cast votes in the

Hough space.

a) an eye image b) corresponding edge map c) edge map with only horizontal gradients d) edge map

with only vertical gradients.

Daugman’s Integro-differential Operator

Daugman makes use of an integro-differential operator for locating the circular iris and pupil

regions, and also the arcs of the upper and lower eyelids. The integro-differential operator is

defined as

where I(x,y) is the eye image, r is the radius to search for, Gσ(r) is a Gaussian smoothing

function, and s is the contour of the circle given by r, x0, y0. The operator searches for the

circular path where there is maximum change in pixel values, by varying the radius and centre x

and y position of the circular contour. The operator is applied iteratively with the amount of

smoothing progressively reduced in order to attain precise localization.

The operator serves to find both the pupillary boundary and the outer (limbus) boundary of the

iris, although the initial search for the limbus also incorporates evidence of an interior pupil to

improve its robustness since the limbic boundary itself usually has extremely soft contrast when

long wavelength NIR illumination is used. Once the coarse-to-fine iterative searches for both

these boundaries have reached single-pixel precision, then a similar approach to detecting

curvilinear edges is used to localize both the upper and lower eyelid boundaries.

The path of contour integration is changed from circular to arcuate, with spline parameters

fitted by statistical estimation methods to model each eyelid boundary. Images with less than

50% of the iris visible between the fitted eyelid splines are deemed inadequate, e.g., in blink.

The result of all these localization operations is the isolation of iris tissue from other image

regions, by the graphical overlay on the eye.

Isolation of the iris from the rest of the image. The white graphical overlays signify detected iris

boundaries resulting from the segmentation process.

Figure - Stages of segmentation with original eye image Top right) two circles overlaid for iris and pupil

boundaries, and two lines for top and bottom eyelid Bottom left) horizontal lines are drawn for each

eyelid from the lowest/highest point of the fitted line Bottom right) probable eyelid and specular

reflection areas isolated (black areas).

2.3.2:Image 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 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.

This is done by following Technique

� Daugman’s Rubber Sheet Model

The homogenous rubber sheet model assign, to each point in the iris , regardless of iris size, a

pair of dimensionless real coordinates(r, θ) where r lies in the unit interval (0,1) & θ is the angle

(0,2π).The remapping or normalization of the iris image I(x,y) from raw coordinates (x,y) to a

doubly dimensionless and non concentric coordinate system (r, θ) can be represented as :-

I(x(r, θ),y(r, θ)) �I(r, θ)

Where I(x,y) are original iris region Cartesian coordinates (xp (θ),yp(θ)) are coordinates of

pupil,

(xs (θ),ys (θ)) are the coordinates of iris boundary along the θ direction and determined by:

x(r, θ) = (1-r)xp (θ) + rxs (θ)

y(r, θ) = (1-r)yp (θ) + rys (θ)

The iris region is modelled as a flexible rubber sheet anchored at the iris boundary with the pupil

centre as the reference point.

Illustration of the normalization process for two images of the same iris taken under varying conditions

Normalization of two eye images of the same iris is shown in Figure . The pupil is smaller in the

bottom image, however the normalization process is able to rescale the iris region so that it has

constant dimension.

2.3.3 Feature Encoding

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

template that is generated in the feature encoding process will also need a corresponding

matching metric, which gives a measure of similarity between two iris templates.

Each isolated iris pattern is then demodulated to extract its phase information using quadrature

2D Gabor wavelets. This encoding process amounts to a patch-wise phase quantization of the

iris pattern, by identifying in which quadrant of the complex plane each resultant phasor lies

when a given area of the iris is projected onto complex-valued 2DGabor wavelets:

where h{Re;Im} can be regarded as a complex-valued bit whose real and imaginary parts are

either 1 or 0 depending on the sign of the 2D integral; is the raw iris image in a

dimensionless polar coordinate system that is size- and translation-invariant, and which also

corrects for pupil dilation as explained in a later section; α and β are the multi-scale 2D wavelet

size parameters, spanning an 8-fold range from 0.15 to 1:2 mm on the iris; Ѡ is wavelet

frequency, spanning three octaves in inverse proportion to β, and represent the polar

coordinates of each region of iris for which the phasor coordinates h{Re; Im} are computed.

Only phase information is used for recognizing irises because amplitude information is not very

discriminating, and it depends upon extraneous factors such as imaging contrast, illumination,

and camera gain. The phase bit settings which code the sequence of projection quadrants as

shown in Fig. The extraction of phase has the further advantage that phase angles are assigned

regardless of how poor the image contrast may be.

The phase demodulation process used to encode iris patterns. Local regions of an iris are

projected onto quadrature 2D Gabor wavelets, generating complex-valued projection

coefficients whose real and imaginary parts specify the coordinates of a phasor in the complex

plane. The angle of each phasor is quantized to one of the four quadrants, setting two bits of

phase information. This process is repeated all across the iris with many wavelet sizes,

frequencies, and orientations, to extract 2048 bits.

Matching

For matching , a test of statistical independence is required which helps to compare the phase

codes for 2 different eyes. The test of statistical independence is implemented by the simple

Boolean Exclusive OR operator (XOR) applied to 2048 bit phase vectors that encode any 2 iris

templates, masked by both of their corresponding mask bit vectors to prevent non iris artifacts

from influencing iris comparison. The XOR operator detects disagreement between any

corresponding pair of bits, while AND operator ensures that the compared bits are not

corrupted by eyelashes etc. The norms(|| ||) of resultant bit vector and the AND ed mask

vector are computed to determine a fractional Hamming distance.

Hamming distance is the measure of dissimilarity between any 2 irises.

HD= ||(code A XOR code B) AND (mask A AND mask B)||

||( mask A AND mask B)||

Where {code A, code B} are phase code vectors bit And {mask A ,mask B} are mask bit vectors.

We can see that the numerator will be the number of differences between the mutually non-

bad bits of code A and code B and that the denominator will be the number of mutually non-bad

bits. If HD result is 0 it is a perfect match.

The Hamming distance was chosen as a metric for recognition, since bit-wise

comparisons were necessary. The Hamming distance algorithm employed also incorporates

noise masking, so that only significant bits are used in calculating the Hamming distance

between two iris templates. Now when taking the Hamming distance, only those bits in the iris

pattern that correspond to ‘0’ bits in noise masks of both iris patterns will be used in the

calculation.In order to account for rotational inconsistencies, when the Hamming distance of

two templates is calculated, one template is shifted left and right bit-wise and a number of

Hamming distance values are calculated from successive shifts. This bit-wise shifting in the

horizontal direction corresponds to rotation of the original iris region by an angle given by the

angular resolution used.

If an angular resolution of 180 is used, each shift will correspond to a rotation of 2

degrees in the iris region. This method is suggested by Daugman , and corrects for

misalignments in the normalized iris pattern caused by rotational differences during imaging.

From the calculated Hamming distance values, only the lowest is taken, since this corresponds to

the best match between two templates. The number of bits moved during each shift is given by

two times the number of filters used, since each filter will generate two bits of information from

one pixel of the normalized region. The actual number of shifts required to normalize rotational

inconsistencies will be determined by the maximum angle difference between two images of the

same eye, and one shift is defined as one shift to the left, followed by one shift to the right. The

shifting process for one shift is illustrated in Figure below.

Fig:--An illustration of the shifting process. One shift is defined as one shift left, and one shift right of a

reference template. In this example one filter is used to encode the templates, so only two bits are

moved during a shift. The lowest Hamming distance, in this case zero, is then used since this corresponds

to the best match between the two templates.

2.4 Biometric System Performance

The following are used as performance metrics for biometric systems:[4]

� false accept rate or false match rate (FAR or FMR): the probability that the system

incorrectly matches the input pattern to a non-matching template in the database. It measures

the percent of invalid inputs which are incorrectly accepted.

� false reject rate or false non-match rate (FRR or FNMR): the probability that the system

fails to detect a match between the input pattern and a matching template in the database. It

measures the percent of valid inputs which are incorrectly rejected.

� equal error rate or crossover error rate (EER or CER): the rate at which both accept and

reject errors are equal. The value of the EER can be easily obtained from the ROC curve. The

EER is a quick way to compare the accuracy of devices with different ROC curves. In general, the

device with the lowest EER is most accurate.

� failure to enroll rate (FTE or FER): the rate at which attempts to create a template from

an input is unsuccessful. This is most commonly caused by low quality inputs.

� failure to capture rate (FTC): Within automatic systems, the probability that the system

fails to detect a biometric input when presented correctly.

2.5 Decision Envirnoment

The performance of any biometric identification scheme is characterized by its “Decision

Environment’. This is a graph superimposing the two fundamental histograms of similarity that

the test generates: one when comparing biometric measurements from the SAME person

(different times, environments, or conditions), and the other when comparing measurements

from DIFFERENT persons. When the biometric template of a presenting person is compared to a

previously enrolled database of templates to determine the Persons identity, a criterion

threshold (which may be adaptive) is applied to each similarly score. Because this determines

whether any two templates are deemed to be “same” or “different”, the two fundamental

distributions should ideally be well separated as any overlap between them causes decision

errors.

3. ADVANTAGES AND DISADVANTAGES

A critical feature of this coding approach is the achievement of commensurability among

iris codes, by mapping all irises into a representation having universal format and constant

length, regardless of the apparent amount of iris detail. In the absence of commensurability

among the codes, one would be faced with the inevitable problem of comparing long codes with

short codes, showing partial agreement and partial disagreement in their lists of features.

Advantages

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

sensitive membrane. This feature makes it advantageous from finger print.

� Flat , geometrical configuration controlled by 2 complementary muscles control the

diameter of the pupil makes the iris shape more predictable .

� An iris scan is similar to taking a photograph and can be performed from about 10 cm to

a few meters away.

� Encoding and decision-making are tractable .

� Genetic independence no two eyes are the same.

DISADVANTAGES

� The accuracy of iris scanners can be affected by changes in lightning.

� Obscured by eyelashes, lenses, reflections.

� Deforms non-elastically as pupil changes size.

� Iris scanners are significantly more expensive than some other form of biometrics.

� 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.

APLLICATIONS

Iris-based identification and verification technology has gained acceptance in a number of

different areas. Application of iris recognition technology can he limited only by imagination. The

important applications are those following:--

� Used in ATM’s for more secure transaction.

� Used in airports for security purposes.

� Computer login: The iris as a living password

� Credit-card authentication

� Secure financial transaction (e-commerce, banking).

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

� Driving licenses and other personal certificates.

� Entitlements and benefits authentication.

� Forensics, birth certificates, tracking missing or wanted person

CONCLUSIONS

There are many mature biometric systems available now. Proper design and implementation of

the biometric system can indeed increase the overall security. There are numerous conditions

that must be taken in account when designing a secure biometric system. First, it is necessary to

realize that biometrics is not secrets. This implies that care should be taken and it is not secure

to generate any cryptographic keys from them. Second, it is necessary to trust the input device

and make the communication link secure. Third, the input device needs to be verified .

Iridian process is defined for rapid exhaustive search for very large databases: distinctive

capability required for authentication today. The extremely low probabilities of getting a false

match enable the iris recognition algorithms to search through extremely large databases, even

of a national or planetary scale. As iris technology superiority has already allowed it to make

significant inroads into identification and security venues which had been dominated by other

biometrics. Iris-based biometric technology has always been an exceptionally accurate one, and

it may soon grow much more prominent.

REFERENCES

1. J. G. Daugman, “High confidence visual recognition of persons by a test of statistical

independence,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 11,

pp. 1148–1161, 1993.

2. J. G. Daugman, “How iris recognition works,” IEEE Transactions on Circuits and System for

Video Technology”, vol. 14, no. 1, pp. 21-30, 2004.

3. Amir Azizi and Hamid Reza Pourreza,, “Efficient IRIS Recognition Through Improvement

of Feature Extraction and subset Selection”, (IJCSIS) International Journal of Computer Science

and Information Security, Vol. 2, No.1, June 2009.

4. www.wikipedia.com

5. Parvathi Ambalakat,” Security of Biometric Authentication Systems”.

6. John Daugman, The Computer Laboratory, University of Cambridge, Cambridge CB3 0FD, UK,”

The importance of being random: statistical principles of iris recognition”.

7. Li Huixian, Pang Liaojun,” A Novel Biometric-based Authentication Scheme

with Privacy Protection”, 2009 Fifth International Conference on Information Assurance and

Security.

8.www.scribd.com/doc/50033821

9. Somnath Dey and Debasis Samanta,” Improved Feature Processing for Iris Biometric

Authentication System”, International Journal of Electrical and Electronics Engineering 4:2 2010