School of Computer Sciences

Universiti Sains Malaysia

Penang

CST 233

Information Security & Assurance

Assignment 3

TITLE : Biometrics- Iris recognition

STUDENT NAME : SOH SIN SIANG

MATRIC NUMBER : 107630

LECTURER : Dr. Aman Jantan

TABLE OF CONTENT

1. INTRODUCTION………………………………………………………………………………3

2. BASIC CONCEPT OF BIOMETRIC TECHNOLOGY…………………………………4-6

3. WHY IRIS RECOGNITION IS THE BEST OPTION?……………………………….7

4. IRIS STRUCTURE………………………………………………………………………………8-9

5. HOW DOES IRIS RECOGNITION WORKS?

Capturing the image…………………………………………………………………………10-11

Defining the location of iris……………………………………………………………….11-14

Feature extraction and matching……………………………………………………….15

6. SPOOFING OF IRIS RECOGNITION……………………………………………………16

7. CASE STUDY ON HOW TO PREVENT SPOOFING ON IRIS

RECOGNITION…………………………………..………………………………………..17-18

8. CONCLUSION…………………………………………………………………………………..19

9. REFERENCES……………………………………………………………………………………20

Introduction

Today‟s information security are in critical need of finding accurate, secure and cost

effective alternatives to passwords an personal identification numbers(PIN) as the

financial losses and identity theft cases increased dramatically year over year from

computer based fraud such as computer hacking. Biometrics solution address these

fundamental problems, because an individual‟s biometric data is unique. An

individual‟s behavioral or physiological characteristics have the capability to reliably

distinguish between authorized person and an imposter. Since biometric

characteristics are distinctive, cannot be forgotten or lost and the person to be

authenticated needs to be physically present at the point of identification. Biometric

is more reliable and are capable than traditional knowledge based on token based

techniques. Biometrics includes fingerprints, retina, iris, voice, signatures, facial,

thermogram, hand geometry, and etc. Among all biometrics eye biometrics such as

iris recognition has attracted a lot of attention because it has various advantages like

greater speed, simplicity and accuracy as compared to other biometric techniques.

Iris recognition relies on the unique pattern of the human iris to identify or verify.

Basic concept of biometric technology

Verification and identification

Biometrics system can be used to verify and identify the person. The most common

use of biometris is verification. Biometrics system verifies user based on the

information provided by the user. for example, when person A enters his/her

username and password, the biometric system then fetches the template for person

A. If there is a match, the system verifies that the user is in fact person A.

identification is used to determine who the subject is without information from the

subject. Identification is complicated because the system must perform a one-to

many comparisons of images, rather than a one to one comparison performs by a

verification system.

Biometric error analysis

A biometric system‟s accuracy is determined by combining the rates of

false acceptance and rejection. A system that is highly calibrated to

reduce the false acceptances may also increase the false rejection,

resulting in more help desk calls and administrator intervention. Each

error presents a unique administrative challenge. Therefore,

administrators must clearly understand the value of the information or

system to be protected, and then find balance between acceptances and

rejection rates appropriates to that value. A poorly created enrolment

template can compound false acceptance and rejection. For example, if a

user enrols in the system with dirt on his finger, it may create an

inaccurate template that does not match a clean print. Natural changes in

a user‟s physical traits may also lead to errors. The point of intersection

is called the crossover accuracy of the system. As the value of the

crossover accuracy becomes higher, the inherent accuracy of the

biometric increases. Table (1) shows crossover accuracy of the different

biometrics technology.

Table 1 Crossover accuracy of the different biometrics technology.

Biometrics Crossover accuracy

Retinal recognition 1:10000000

Iris recognition 1:131000

Fingerprints 1:500

Hand geometry 1:500

Signature dynamics 1:50

Voice dynamics 1:50

How basically biometric system work?

Figure (1) describes the process involved in using a biometrics system for security. It

contains nine steps.

(1) Capture the chosen biometric;

(2) Process the biometric so as to extract and enrol the template;

(3) Store the template in a local repository, a central repository, or a portable token

such as smart card;

(4) Live-scan the chosen biometric;

(5) Process the biometric and extract the biometric template;

(6) Store the reference template;

(7) Match the scanned biometric against stored templates;

(8) Provide a matching score to use for decision making;

(9) Record a secure audit trail with respect to system.

1. Biometric

devices

3. Trial template 2. Biometric

process

9. Decision 7. Matching 8. Score

4. Biometric

devices

5. Biometric

process

6. Reference

template

Why iris recognition is the best option?

Based on the table 1, we can see that, apart from eye recognition, others

recognition crossover accuracy is relatively low compared to eye recognition. For

example in face recognition, the crossover accuracy is low as the difficulties arise

from the face that the face is a changeable social organ displaying a variety of

expressions. It has been shown that for facial images taken at least one year apart;

even the best algorithms have error rates of 43% to 50%.

Retina recognition however producing a higher accuracy of recognition compared to

iris. But the problem arises during the biometric process to take the trial template.

For example, the user-reader interfaces is not convenient for eyeglass wearers

(glasses have to be removed first) nor for those who have concerns about close

contact with the reader( eye infection). Users must interact correctly and patiently

for the system to work. Of all the biometric technologies, the motivation level of the

user of retinal recognition must be very high for the system to function properly.

For all of these reasons, iris patterns become interesting as an alternative approach

to reliable visual recognition of persons when imaging(trial template) can be done at

distances of less than a meter. As an internal yet extremely visible organ of the eye,

iris is well protected from the environment and stable over time. We will discuss

about the iris structure in the next section to know in deep how iris is used in

biometrics recognition.

Iris structure

The iris is the colour part of the eye behind the eyelids, and in front of the

lens. It is the only internal organ of the body, which is normally externally visible.

These visible patterns are unique to all individuals and it has been found that the

probability of finding two individuals with identical iris patterns is almost zero.

Although the human eye is slightly asymmetrical and the pupil is slightly off the

centred, for the most practical cases we think of the human eye is symmetrical

with respect to line of sight. The iris controls the amount of light that reaches the

retina. Due to heavy pigmentation, light pass only through the iris via pupil, which

contracts and dilates according to the amount of available light. Iris dimensions

vary slightly between the individuals. Its shape is conical with the papillary margin

located more interiorly than the root. A thickened region called the collarete divides

the anterior surface into the ciliary and pupil zones.

Iris is made up of four different layers. The back layer is heavily pigmented and

makes iris opaque so that light only reaches the eye through the pupil. The next

layer contains the sphincter and the dilator muscles that allows for contraction and

dilation. The third layer is the stroma, which is loosely connected tissue containing

collagen, melanocytes, most cells and macrophases. The exterior layer is called the

anterior border layer and is denser than the previous layer with more pigmentation.

The colour of the iris is created by different levels of light absorption in the anterior

border layers, little pigmentation in this layer results in a blue appearance because

light reflects from the back layer of the iris. The more pigmentation a person has in

the anterior border layer, the darker is the iris.

How does iris recognition work?

Iris recognition basically can be separated into three parts:

1. Capturing the image

2. Defining the location of the iris

3. Feature extraction and Matching

Capturing the image

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. Given that the

iris is a relatively small, dark object and that human operators are very sensitive

about their eyes, this matter require careful engineering. Several points are of

particulat concern. First, it is desirable to capture images of the iris with sufficient

resolution and sharpness to support recognition. Secondly, it is important to have

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

that annoys the operator. Thirdly, these images must be well framed (centred).

Further, as an integral part of this process, artifacts/artefacts( noise or error due to

specular reflections, optical aberrations, etc.) in the captured images should be

eliminated as much as possible. For graphical illustration, expected captured image

should be about the same as shown as figure 1.

Figure 1 example of captured iris image. Imaging of the iris must acquire sufficient

detail for recognition while being minimally invasive to the operator. Image

acquisition yields an image of the iris as well as the surrounding region.

Defining the location of iris

Without placing undue constraints on the human operator, capturing image of the

iris cannot be expected to yield an image containing only the iris. Rather, process of

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

derived for the immediately surrounding eye region. Therefore, prior to performing

iris pattern matching, it is important to localize that portion of the captured image

that corresponds to an iris. In particular, it is necessary to localize that portion of the

image derived from inside the limbus( the border between sclera and the iris) and

outside the pupil. Further, if the eyelids are covering part of the iris, then only that

portion of image below the upper eyelid and above eyelid should be included.

Typically, the limbic boundary is imaged with high contrast, owing the sharp change

in eye pigmentation that it marks. The upper and lower portions of this boundary,

however can be covered by the eyelids. The pupillary boundary can be far less well

defined. The image contrast between a heavily pigmented iris and its pupil can be

quite small. Further, while the pupil typically is darker than the iris, the reverse

relationship can hold in cases of cataract; the clouded lens leads to significant

amount of backscattered light. Like the pupillary boundary. Eyelid contrast can be

quite variable depending on the relative pigmentation in the skin and the iris. The

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

Taken into consideration, these observations suggest that iris localization must be

sensitive to wide range of edge contrast, robust to irregular borders, and capable of

dealing with variable occlusion. Three steps below are usually taken when come to

the phase of defining the location of iris:

1. Binary segmentation

2. Pupil center localization

3. Circular edge detection

Figure 2: this figure shows how binary segmentation and limbic boundary was

detected. The eye image (a) was unwrapped into polar coordinates(c) and

localization of the limbic boundary of carried out (d). Iris segment obtained in (e).

Figure 3: the result of the pupil center localization and also circular edge detection

on the image that obtain in the first stage.

Figure 4 : Result of iris localization. Given a captured image, it is necessary to

separate the iris from the surround. The input to the localization process was the

captured iris image of figure 1. After localization, all but the iris is masked out.

Feature extraction and Matching

Having localized the region of an acquired image that corresponds to the iris, the

final task is to decide if this pattern matches a previously stored iris pattern. This

matter of pattern matching can be decomposed into four parts:

1. Bringing the newly acquired iris pattern into spatial alignment with a

candidate data base entry;

2. Choosing a representation of the aligned iris patterns that makes their

distinctive patterns apparent;

Figure 5: encoded iris patterns of the newly acquired image.

3. Evaluating the goodness of match between the newly acquired and database

representations;

4. Deciding if the newly acquired data and the database entry were derived from

the same iris based on the goodness of match.

Spoofing of iris recognition

A spoof is a counterfeit biometric that is used in an attempt to circumvent a

biometric device. Even though iris recognition provide a highly accuracy and security

to the authentication system, it is however still prone to the attack of spoofing. A

straight forward method that has been used to spoof an iris recognition device is

based on a high quality photo graph of the eye. Unauthorized user just needs to

print the authorized iris image on a paper with a laser printer and place it in front of

the iris recognition device, and the device will be spoofed. „Replay attack‟ is one of

the spoofing methods too. Another method used to successfully spoof some iris

recognition device is to use a contact lens on which an iris pattern is printed. Even

more sophisticated, multi-layered and three-dimensional artificial irises may also be

produces to spoof an iris recognition device.

Figure 6: Natural iris (left) and clone iris/contact lens(right)

Case study on how to prevent spoofing on iris recognition

In paper [6], it provides several approaches to prevent spoofing on iris recognition.

Below are the summary of the paper on the approaches of preventing spoofing of

iris recognition:

Aim: To detect whether the eyes are alive or not, aliveness detection

Suggested method:

Based on frequency analysis(FA)

Detect artificial frequencies in iris images that may exist due to the finite

resolution of the printing devices

Controlled light reflection(CLR)

Relied on the detection of infrared light reflections on the moist corneas when

stimulated with light sources positioned randomly in space.

Pupil dynamics(PD)

Employs a model of the human pupil response to light changes. Comparison

between the real pupil and the observed object.

Aim : To prevent ‘replay attack’ by stopping the electronic replay of an

authentication procedure.

Suggested method:

Zak-Gabor based coding

Use Zak transform to convert iris stripes into Gabor-transformation coefficients. The

coefficient produced has more advantages compared to Gabor filtering (typically

used in commercial system).

Conclusion

A biometric system provides automatic identification of an individual based on

a unique feature or characteristics possessed by the individual. Iris is a useful

biometric for recognition system. It is simple, easy to use, high accuracy, and cost

effective compared to the other biometrics.

Discussion on how iris recognition works has been discussed in this paper to

get a deeper understanding of iris recognition. Three main steps are included in the

process, they are: Capturing the image, defining the location of the iris, feature

extraction and matching. Besides that, spoofing of iris recognition and ways to

overcome it are also included in the case study section.

References

[1]Nicolaie Popescu-Bodorin, http://fmi.spiruharet.ro/bodorin/articles/fbvme-csir-

buid-rj.pdf Date of accessed: 12/5/2012

[2]http://en.wikipedia.org/wiki/Iris_recognition Date of accessed: 12/5/2012

[3]Richard P.Wilders “Iris Recogniton: An Emerging Biometric Technology”

[4]John Daugman,” iris recognition for personal identifications.”

http://www.cl.cam.ac.uk/~jgd1000/iris_recognition.html

[5]John Daugman(2004), “How iris recognition works”

[6] Adam Czajka, Przemek Strzelczyk, and Andrzej Pacut,“Making iris recognition

more reliable and spoof resistant”

http://spie.org/documents/Newsroom/Imported/0614/0614-2007-06-15.pdf

Figure:http://www.cytrap.eu/files/ReguStand/2007/image/2007-11-28_iris-

recognition-biometric-passport.jpg

Figure 1: http://sailjamehra.files.wordpress.com/2009/05/2.png

Figure 3: http://docsdrive.com/images/ansinet/itj/2009/fig3-2k9-9541.gif

Figure 4: http://ars.sciencedirect.com/content/image/1-s2.0-S026288561000079X-

gr8.jpg

Figure 5: http://www.morpho.com/IMG/jpg/iris.jpg

Figure 6: http://binary-services.sciencedirect.com/content/image/1-s2.0-

S0031320311003074-gr2.sml