A SEMINAR REPORT ON IRIS RECOGNITION: A TOOL FOR MODERN SECURITY

A SEMINAR REPORT ON IRIS RECOGNITION: A TOOL FOR MODERN SECURITY

IRIS RECOGNITION: A TOOL FOR MODERN SECURITY

A SEMINAR PRESENTED BY:

N**** L****, I*******

REG. NO.: CS/12/***

SUBMITTED TO:

DEPARTMENT OF COMPUTER SCIENCE

FACULTY OF SCIENCE

MADONNA UNIVERSITY, ELELE CAMPUS, RIVERS STATE

IN PARTIAL FUFILMENT FOR THE REQUIREMENT OF THE AWARD OF BACHELOR OF SCIENCE (B.Sc.) DEGREE IN COMPUTER SCIENCE.

DECEMBER, 2015

DECLARATION

This is to certify that the seminar research on “IRIS RECOGNITION: A TOOL FOR MODERN SECURITY ” was carried out by N**** L**** I*******, with REGISTRATION NUMBER: CS/12/***, has met the condition of Bachelors of science (B.Sc) degree in Computer Science, Madonna University Okija, Elele Campus. It is hereby approved for its contribution to knowledge.

N**** L**** I******* ………….….………..... ......……………………

(Student) Signature Date

MRS C******* I******** ………….………….....… …………......…………

(Supervisor) Signature Date

MRS E******* *** …………......…………… …………………......….

(AG Head of Department) Signature Date

DEDICATION

I dedicate this work to Almighty God for his protection and good health He bestow me throughout the duration of this programme.

ACKNOWLEDGEMENTS

I cannot but make specific references to those people who contributed greatly towards the successful completion of this study.

I wish to acknowledge with gratefulness the special effort of my Supervisor, MRS C******* who contributed greatly to the successful completion of this seminar research. I am grateful for her guidance and assistance, especially her patience in reading and examining thoroughly every aspect of the work in making necessary corrections, an act which fully attests to her academic sagacity.

I also wish to thank all the Computer Science lecturers, especially my AG HOD MRS E******* O**, and all lecturers in the Faculty of Sciences for their blissful contributions towards my academic success. I am also sincerely grateful to my parents MR and MRS.N**** C.U., my brother E**** for their moral, financial and spiritual support.

TABLE OF CONTENTS

TITLE PAGE ………………………………………………………………………………….i

DECLARATION ……………………………………………………………………………..ii

DEDICATION ……………………………………………………………………………….iii

ACKNOWLEDGEMENT …………………………………………………………………...iv

TABLE OF CONTENT ………………………………………………………………………v

ABSTRACT ………………………………………………………………………………...viii

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

1.1 BACKGROUND OF THE STUDY ……………………………………………….....1

1.2 AIM AND OBJECTIVES OF THE STUDY ………………………………………...2

1.3 SIGNIFICANT OF THE STUDY …………………………………………………….2

1.4 SCOPE OF THE STUDY …………………………………………………………… 2

1.5 LIMITATION OF THE STUDY ……………………………………………………..2

1.7 GLOSSARY …………………………………………………………………………..3

1.6 ORGANIZATION OF THE STUDY ………………………………………………...3

CHAPTER TWO ……………………………………………………………………………...5

LITEREATURE REVIEW …………………………………………………………………...5

2.1 HISTORY OF IRIS RECOGNITION …………………………………………….......5

2.2 RELATED WORK …………………………………………………………………...5

CHAPTER THREE …………………………………………………………………………...7

WHAT IS IRIS RECOGNITION …………………………………………………………….7

3.1 IRIS RECOGNITION ………………………………………………………………...7

3.2 IRIS CHARACTERISTICS …………………………………………………………..7

3.2.1 Iris characteristics that make it appealing for authentication are: …………………….8

3.3 WHY IRIS??? ………………………………………………………………………...8

3.4 COMPARISON WITH OTHERS BIOMETRIC SYSTEM ………………………….9

3.4.1 IRIS RECOGNITION vs FACIAL RECOGNITION ………………………………...9

3.4.2 IRIS RECOGNITION vs FINGERPRINT …………………………………………...9

3.4.3 IRIS RECOGNITION vs RETINA RECOGNITION ………………………………..9

v 

3.4.4 IRIS RECOGNITION vs HAND GEOMETRY …………………………………….10

3.5 ARCHITECTURE OF IRIS RECOGNITION ……………………………………...10

3.6 DEVICES USED FOR IRIS RECOGNITION ……………………………………...11

3.6.1 Characteristics of the Iris Recognition Devices are; ………………………………...12

3.7 COMPONENT OF IRIS RECOGNITION ………………………………………….12

3.7.1 IMAGE PREPROCESSING: ………………………………………………………..12

Iris Localization: …………………………………………………………………………….12

Iris Normalization: …………………………………………………………………………..13

3.7.2 FEATURES EXTRACTION ………………………………………………………..13

3.7.3 PATTERN MATCHING ……………………………………………………………13

3.8 HOW IRIS RECOGNITION WORKS ……………………………………………..13

3.8.1 CAPTURING THE IMAGE: ………………………………………………………..14

3.8.2 DEFINING THE LOCATION OF THE IRIS AND OPTIMISING THE IMAGE …14

3.8.3 STORING AND COMPARING THE IMAGE ……………………………………..15

3.9 APPLICATION AREA ……………………………………………………………...17

3.9.1 ATM’S AND IRIS RECOGNITION: ……………………………………………….17

3.9.2 TRACKING PRISONER MOVEMENT ……………………………………………17

3.9.3 NATIONAL BORDER CONTROLS USING IRIS RECOGNITION ……………...18

3.10 ADVANTAGES OF IRIS RECOGNITION ………………………………………...19

3.11 DISADVANTAGES OF IRIS RECOGNITION ……………………………………19

CHAPTER FOUR …………………………………………………………………………...21

SUMMARY, CONCLUSION AND RECOMMENDATION ……………………………...21

4.1 SUMMARY …………………………………………………………………………21

4.2 CONCLUSION ……………………………………………………………………...21

4.3 RECOMMENDATION ……………………………………………………………..22

REFERENCE ………………………………………………………………………………..23

ABSTRACT

Iris recognition is a biometric technology for identifying humans by capturing and analysing the unique patterns of the iris in the human eye. A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Unlike other biometric such as fingerprints and face recognition, the distinct aspect of iris comes from randomly distributed features. Iris recognition is regarded as the most reliable and accurate biometric identification system available. This seminar work shows how Iris recognition work, its comparison with other biometric device and some of its application areas such as Automated Teller Machine (ATM), Tracking Prisoner Movement, National Border Control, Ticketless air travel and Premises access control. Finally, some advantages include protection, speed, accuracy, scalability and stability, and some disadvantages are blindness, infection, and expensive equipment etc. In conclusion, it provides an accurate and secure method of authenticating users onto company systems.

CHAPTER ONE

INTRODUCTION

1.1 BACKGROUND OF STUDY

In today’s information technology world, security for systems is becoming more and more important. The number of systems that have been compromised is ever increasing and authentication plays a major role as a first line of defence against intruders. The three main types of authentication are something you know (such as a password), something you have (such as a card or token), and something you are (biometric). Passwords are notorious for being weak and easily crackable due to human nature and our tendency to make passwords easy to remember or writing them down somewhere easily accessible. Cards and tokens can be presented by anyone and although the token or card is recognizable, there is no way of knowing if the person presenting the card is the actual owner. Biometrics, on the other hand, provides a secure method of authentication and identification, as they are difficult to replicate and steal.

Iris recognition is a biometric technology for identifying humans by capturing and analyzing the unique patterns of the iris in the human eye. Iris recognition can be used in a wide range of applications in which a person’s identity must be established or confirmed. For example, these include passport control, border control, frequent flyer service, premises entry, access to privilege information, computer login or any other transaction in which personal identification and authentication relies on knowledge-based or token-based passwords. Nevertheless, one of the most dangerous security threats in today’s world is impersonation, in which somebody claims to be someone else. Through impersonation, a high-risk security area can be at risk. An unauthorized person may get access to confidential data or important documents can be stolen. Normally, impersonation is tackled by identification and secure authentication, however, the traditional knowledge-based (password) or possession-based (ID, Smart Card) methods are not sufficient since they can be easily hacked or compromised. Hence, there is an essential need for personal characteristics-based (biometric) identification due to the fact that it can provide the highest protection against impersonation. 

1.2 PROBLEM STATEMENT 

Due to increased rate of crime, inaccuracy, unreliability, inadequacy of other biometric techniques and the use of weak password which can be easily cracked, the quest for strong authentication is needed and that is where Iris Recognition comes in.

1.3 OBJECTIVES OF STUDY

This seminar topic is generally conducted to understand Iris Recognition, a tool for modern security. Other specific objectives include;

· To analyze the impact of Iris recognition in checkmating impersonation.

· To Compare Iris Recognition with others biometric system

· To examine the possibility of using Iris Recognition in curtailing crime.

1.4 SIGNIFICANT OF STUDY

This seminar presentation is significant in several aspects. Apart from being a necessary condition in Madonna University, it is also an opportunity for the researcher to conduct an independent study based on empirical data.

Furthermore, it will help to advance knowledge and hence, be of great value to other researchers taking computer science as a course and researching on related issues and to members of the academic in general.

Finally recommendations made in this study will serve as possible solutions to appropriate agencies in adopting Iris Recognition in curtailing crimes and impersonation.

1.5 SCOPE OF STUDY

The scope of the study was restricted to Iris Recognition, it will focus on Iris Rsecognition as a biometric technology, and comparison with other biometric technology such as Finger Print, Hand Geometry, Facial Recognition and Retina Recognition etc.

1.6 LIMITATION OF STUDY

This study is designed specifically to examine Iris Recognition, a tool for modern security, hence because of research constraints such as time which is of essence and no researcher have the luxury of it all and finance that is usually inadequate and limited at the disposal of researcher.

1.7 GLOSSARY

· BIOMETRICS: biometrics, refers to the automatic recognition of individuals based on their physiological and/or behavioural characteristics (Vijay Dhir et al, 2010)

· IRIS: Iris is a coloured oval-to round-shaped ring surrounding the pupil of the eye. It consists of muscles that adjust the size of the pupil (Vijay Dhir et al, 2010).

· RECOGNITION: is used as a form of identification and access control.

· AUTHENTICATION: A process by which subjects, normally users, establish their identity to a system. This may be affected by the use of a password or possession of a physical device (Elgamal, 2013).

· ENROLLMENT: The process of adding a user’s credentials to the authentication system. 

· VERIFICATION: A one to one comparison of a captured biometric with a stored template to verify that the individual is who he claims to be. Can be done in conjunction with a smart card, username or ID number. (Vijay Dhir et al, 2010)

· IDENTIFICATION: A one to many comparisons of the captured biometric against a biometric database in attempt to identify an unknown individual. The identification only succeeds in identifying the individual if the comparison of the biometric sample to a template in the database falls within a previously set threshold (Vijay Dhir et al, 2010).

1.6 ORGANIZATION OF STUDY

For systematic presentation, this work is planned in four chapters. Each chapter deals with a particular aspect or segment of the work.

CHAPTER ONE starts by introducing the topic, which gives an insight and in-depth analysis of the seminar work, which was closely followed by the objectives of the topic, which gave rise to the significance of the topic, followed by the limitation, scope and crowned by the organization of the topic.

CHAPTER TWO deals with the historical background of the topic and followed by the definition of terms of the topic.

CHAPTER THREE focuses on the discussion of the topic, which was closely followed by what is Iris Recognition, Architecture of Iris Recognition, Component of Iris Recognition, how Iris Recognition is a tool for modern security work, the Application of Iris Recognition in modern technology which is followed by the Positive Effect and Negative Effect of Iris Recognition.

Finally, CHAPTER FOUR summaries the topic, enumerate conclusion and proffers recommendation for the topic and the way forward.

CHAPTER TWO

LITERATURE REVIEW

2.1 HISTORY OF IRIS RECOGNITION

The iris recognition technology captures and analyzes the unique features of iris in the human eye to perform identification. In 1936, ophthalmologist Frank Burch proposed the concept of using iris patterns as a method to recognize an individual, the idea appeared in James Bond films, but it still remained science fiction and conjecture. The first claim that no two irises are identical was made by Dr. Leonard Flom and Dr. Aran Safir, both ophthalmologists in mid 1980s. The claim was based on their clinical research that every iris is different and was seen to remain unchanged in clinical photographs. This claim made the human iris as a good candidate for a biometric solution and after substantial research the patent of using iris as a means for identifying persons was awarded to them in 1987. Dr. Flom approached Harvard Professor Dr. John Daugman to develop an algorithm to automate identification of the human iris. Later in 1989 Dr. John Daugman developed algorithms for recognizing persons by iris recognition. In 1993, the Defense Nuclear Agency began work to test and deliver a prototype unit, which was successfully completed by 1995 due to the combined efforts of Drs. Flom, Safir, and Daugman. In 1994, Dr. Daugman was awarded a patent for his automated iris recognition algorithms. In 1995, the first commercial products became available.

2.2 RELATED WORK

According to his introduction, (Elgamal, 2013) Automatic reliable personnel identification systems using biometrics have received a great importance in the past few years. Biometrics refers to a science of analyzing human physiological or behavioural characteristics for security purposes. Biometric technologies are being utilized across a variety of applications. There are many biometric technologies which commonly be used in government, forensics and commercial area (Ross, 2004) such as iris recognition, fingerprints, hand geometry, and DNA (Ghatol, 2007). 

According to Masek (2003), the iris is a thin color circular diaphragm, which can be found between the cornea and the lens of the human eye and close to the pupil. Position of the iris is bounded by the pupil and the sclera (white of the eye) on their surroundings. Iris has much visual information in the texture (Murty, Reddy, and Babu, 2009). 

The pattern of iris forms from the third month of gestation and complete these pattern structures in five months, and their pigment accretion can continue until two(2) years old age (Daugman, 2001). In iris recognition there is no change in iris features during a person’s lifetime after two(2) years old age and size of iris can be varying from 10% to 80% with the average diameter is 12mm (Daugman, 2001).

In 1936, ophthalmologist Frank Burch proposed iris pattern for personal recognition. Then in 1987 two ophthalmologists, Aran Safir and Leonard Flom, patented this idea, and they ask John Daugman to create algorithms for iris recognition in 1989 (Daugman, 2001).

Iris provides one of the most stable biometric signals for identification, with a distinctive texture that is formed before age one and remains constant throughout life unless there is an injury to the eye (Ives, 2004). Iris recognition can easily be considered as the most reliable form of biometric technology, compared with other biometric technologies, such as face, and fingerprint recognition (Nasser A. Biqami, 2013).

Most of the currently deployed commercial algorithms for iris recognition (by John Daugman) have a very low false acceptance rate compared to the other biometric identifiers.

Some of the biometric identifiers have problems with replay attacks, for instance fingerprints. Replay attacks with the iris biometric can be check by detecting the aliveness of the eye. The pupil changes its size when light is shone into the eye. The algorithms are able to measure this change in pupil size. The process of capturing the iris image is not intrusive. Iris images can be computer matched more accurately than a face image, and it’s acknowledged that iris recognition is more accurate than any other biometric technique.

According to (Rishabh and Sandeep J. 2012), Iris recognition is considered to be most secure biometric approach as it is non-invasive and stable throughout life. For the purpose of research and development of Iris recognition technology there are few public and freely available databases to have sample images. These iris databases contributes rich amount of iris images which were taken in different environments. In this paper they discuss and compare the main characteristics of the public and freely available iris image databases to find the suitable one to test feature extraction method of iris recognition in non-cooperative environment.

CHAPTER THREE

FINDINGS

3.1 IRIS RECOGNITION

Iris recognition is a type of pattern recognition of a person’s iris recorded in a database for future attempts to determine or recognize a person’s identity when the eye is viewed by a reader. The iris usually has a brown, blue, gray, or greenish color, with complex patterns that are visible upon close inspection. Because it makes use of a biological characteristic, iris recognition is considered a form of biometric verification

Iris recognition combines computer vision, pattern recognition, statistics, and the human machine interface.

3.2 IRIS CHARACTERISTICS

The human iris is a coloured oval – to round-shaped ring surrounding the pupil of the eye. Figure 1 shows a sample iris, it consists of muscles that adjust the size of the pupil. The iris is the only internal body organ that is visible externally. One of the most distinctive characteristics is its stability. The iris pattern stabilizes by the second year of birth and remains unchanged throughout person’s lifetime unless injured or damaged by accident or disease.

( Hussein H. Fakhry and Benedict B. Cardozo, 2006)

Figure 1: The Human Iris

3.2.1 Iris characteristics that make it appealing for authentication are:

The iris pattern is more complex and more random than other biometric patterns and hence offer a highly precise methods for individual authentication with a false acceptance error rate of less than one in two million records.

The iris located in the human eye is protected behind the eyelid, cornea and aqueous. This helps it to keep the damage and abrasion minimal. In addition it is nearly impossible to forge identity.

The iris pattern remains stable and unchanged after the age of two year and does not degrade over time or with the environment.

The probability of two irises producing the same numerical code is almost zero.

A distinctive iris pattern is not liable to theft, loss or compromise.

Each iris is different, even between identical twins or between left and right iris of an individual.

Since the iris is an extremely complex structure, modification of the iris would require sophisticated intricate microsurgery. This could result in individual loss of sight or an obvious artificiality that can be easily seen visually.

3.3 WHY IRIS?

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 than 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, competence in administration, and benefits to users of various systems.

3.4 COMPARISON WITH OTHERS BIOMETRIC SYSTEM

3.4.1 IRIS RECOGNITION vs FACIAL RECOGNITION

· Lighting, age, glasses, and head/face coverings all impact false reject rates in facial recognition whereas iris recognition poses no difficulty in enrolling people that wear glasses or contact lenses.

· Face recognition has Privacy concerns: people do not always know when their picture/image is being taken and being searched in a database or worse, being enrolled in a database whereas in Iris Recognition subjects agree to enroll and participate, reducing privacy concerns.

· Iris recognition is more reliable than facial recognition.

3.4.2 IRIS RECOGNITION vs FINGERPRINT

· Based on occupation, trauma or disease, individual fingerprints may be obscured, damaged or changed meaning some people may need to enroll multiple times over the course of their lives. Fingerprint readability also may be affected by the work an individual does. For example, transportation workers such as mechanics, food workers, or maintenance workers may present fingerprints that are difficult to read due to dryness or the presence of foreign substances, such as oil or dirt, on fingers.

· But fingerprint is not as accurate as iris recognition. Fingerprint false accept rate varies by Vendor, and is approximately 1 in 100,000. Whereas Iris recognition false accept rate is 1 in 1.2 million statistically.

· Iris recognition can perform 1: all matches in a high speed environment, whereas fingerprint searches take much longer, may require filtering, and may return multiple candidate matches.

3.4.3 IRIS RECOGNITION vs RETINA RECOGNITION

· The error rate for retinal scanning is 1:10,000,000 compared to the iris recognition error rate of 1:131,000.

· People wearing glasses must remove them for a retinal scan. For iris recognition, the National Physical Laboratory (NPL) tests found that glasses can make enrolment more difficult, but they can remain in place for verification without causing difficulty.

3.4.4 IRIS RECOGNITION vs HAND GEOMETRY

· Hand size and geometry changes over time, especially in the very young and the very old whereas the iris itself is stable throughout a person’s life (approximately from the age of one); the physical characteristics of the iris don't change with age.

· People are reluctant to place hand where many others have touched so hygiene is another issue with hand geometry, whereas in iris recognition there is no physical contact of person with camera.

· Also extreme sizes are not accommodated in all hand readers.

3.5 ARCHITECTURE OF IRIS RECOGNITION

The block diagram in Figure 2 depicts the principle steps of the proposed iris recognition system and is described in the following. The system has two sub–systems: the iris enrolment system and the iris verification system. The iris enrolment system is to enrol the iris in the database for further identification. The iris verification system compares a newly input iris with the known irises in the database and decides if it is in the database.

The iris enrolment system is comprised of the following modules: The image acquisition module, the Pre-processing Module, the feature extraction module, the Enrolment Module, and the Iris Pattern Database. The iris verification system does not have the Enrolment Module, but has two additional modules: pattern matching module and the Identification Module.

( Yingzi Du and Ives Robert, 2004)

Figure 2: Iris Recognition System

3.6 DEVICES USED FOR IRIS RECOGNITION

devices used for iris recognition

3.6.1 Characteristics of the Iris Recognition Devices are;

· HARMLESS: - Acquiring your iris image through the optical units is completely safe. Capturing the iris image is just like taking a picture.

· CONVENIENT OPERATION: - You just enrol your iris for registration and recognition. The identification process can be performed perfectly, regardless of wearing eyeglasses, most sunglasses, or soft contact lenses.

· ACCURACY: - Iris recognition is based on the most mathematically unique biometric – the iris of the eye. The human iris is unique, even between twins or an individual’s right and left eyes. 

· SCALABILITY: - This can handle sizable database entries and there is not any negative impact on the accuracy as the database size increase.

· SPEEDY IDENTIFICATION: - Identification can be made within 1 to 2 seconds.

3.7 COMPONENT OF IRIS RECOGNITION

The iris recognition consisted of three major components: Image Pre-processing, Feature Extraction and Pattern matching.

3.7.1 IMAGE PREPROCESSING:

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 which are Iris Localization and Iris Normalization.

Iris Localization: Iris localization by definition means to isolate the actual iris region in a digital eye image by detecting the inner and outer boundary of the iris. Figure 3 shows the Iris Localization. The eyelids and eyelashes normally occlude the upper and lower parts of the iris region. A technique is required to isolate and exclude these artefacts as well as locating the circular iris region. The aim of this is to detect the iris portion which can be approximated by two circles, one is the iris/sclera (outer) boundary, and another interior to the first is the iris/pupil (inner) boundary. Iris Localization is done by the process of Iris segmentation which localizes the correct iris region in an eye image. Iris segmentation is an essential in automated iris processing systems, because it is the basis for any further operations. 

( P. Ramamoorthy and R.Krishnamoorthy, 2012)

Figure 3: Localization of Iris

Iris Normalization: 

Once the iris region is segmented, the next stage is to normalize this part so as to enable the generation of the iris-code and their comparisons. Since the variations in the eye, like optical size of the iris, position of pupil in the iris, and the iris orientation change from person to person, it is required to normalize the iris image so that the representation is common to all, with similar dimensions. The normalization process involves un-wrapping the iris and converting it into its polar equivalent.

3.7.2 FEATURES EXTRACTION 

The extraction of iris features means capturing ring-shape patterns around the iris area. After capturing the eye image, the iris area should be correctly extracted from it. Detecting the inner boundary of the iris against the pupil and the outer border of the iris against the sclera finishes the process.

3.7.3 PATTERN MATCHING 

After iris localization, the final step is pattern matching of the iris image which generates a match score by comparing the feature sets of two iris images. One technique for comparing two Iris-Codes is to use the Hamming distance, which is the number of corresponding bits that differ between the two Iris-Codes. The iris pattern is different for every person (even identical twins don’t have the same iris pattern). These patterns are used to create templates for iris recognition. The acquired image is matched with the whole database of templates.

3.8 HOW IRIS RECOGNITION WORKS

The process of capturing an iris into a biometric template is made up of 3 steps:

1. Capturing the image

2. Defining the location of the iris and optimising the image

3. Storing and comparing the image.

3.8.1 CAPTURING THE IMAGE:

The image of the iris can be captured using a standard camera using both visible and infrared light and may be either a manual or automated procedure (Figure 4). The camera can be positioned between three and a half inches and one meter to capture the image. In the manual procedure, the user needs to adjust the camera to get the iris in focus and needs to be within six to twelve inches of the camera. This process is much more manually intensive and requires proper user training to be successful. The automatic procedure uses a set of cameras that locate the face and iris automatically thus making this process much more user friendly.

(Penny Khaw, 2002)

Figure 4: An Iris-Scan model 2100 iris scanner

3.8.2 DEFINING THE LOCATION OF THE IRIS AND OPTIMISING THE IMAGE

Once the camera has located the eye, the iris recognition system then identifies the image that has the best focus and clarity of the iris (Figure 5). The image is then analysed to identify the outer boundary of the iris where it meets the white sclera of the eye, the pupillary boundary and the centre of the pupil. This results in the precise location of the circular iris.

(Penny Khaw, 2002)

Figure 5: Circular Iris Location

The iris recognition system then identifies the areas of the iris image that are suitable for feature extraction and analysis. This involves removing areas that are covered by the eyelids, any deep shadows and reflective areas. The following diagram (Figure 6) shows the optimisation of the image.

(Penny Khaw, 2002)

Figure 6: Optimizing the image

3.8.3 STORING AND COMPARING THE IMAGE

Once the image has been captured, “an algorithm uses 2-D Gabor wavelets to filter and map segments of the iris into hundreds of vectors (known here as phasors). The 2-D Gabor phasor is simply the “what” and “where” of the image. Even after applying the algorithms to the iris image there are still 173 degrees of freedom to identify the iris. These algorithms also take into account the changes that can occur with an iris, for example the pupil’s expansion and contraction in response to light will stretch and skew the iris. This information is used to produce what is known as the Iris-Code, which is a 512-byte record. This record is then stored in a database for future comparison. When a comparison is required the same process is followed but instead of storing the record it is compared to all the Iris-Code records stored in the database. The comparison also doesn’t actually compare the image of the iris but rather compares the hexadecimal value produced after the algorithms have been applied.

In order to compare the stored Iris-Code record with an image just scanned, a calculation of the Hamming Distance is required. The Hamming Distance is a measure of the variation between the Iris-Code record for the current iris and the Iris-Code records stored in the database. Each of the 2048 bits is compared against each other, i.e. bit 1 from the current Iris-Code and bit 1 from the stored Iris-Code record are compared, then bit 2 and so on. Any bits that don’t match are assigned a value of one and bits that do match a value of zero. Once all the bits have been compared, the number of non-matching bits is divided by the total number of bits to produce a two-digit figure of how the two Iris-Code records differ. For example a Hamming Distance of 0.20 means that the two Iris-Codes differ by 20%.

Iris Recognition Process

(Penny Khaw, 2002)

Figure 7: Iris Recognition Process

Figure 7 summarizes the steps to be followed when doing iris recognition.

Step 1: Image acquisition, the first phase, is one of the major challenges of automated iris recognition since we need to capture a high-quality image of the iris while remaining non-invasive to the human operator.

Step 2: Iris localization takes place to detect the edge of the iris as well as that of the pupil; thus extracting the iris region.

Step 3: Normalization is used to be able to transform the iris region to have fixed dimensions, and hence removing the dimensional inconsistencies between eye images due to the stretching of the iris caused by the pupil dilation from varying levels of illumination.

Step 4: The normalized iris region is unwrapped into a rectangular region.

Step 5: Finally, it is time to extract the most discriminating feature in the iris pattern so that a comparison between templates can be done. Therefore, the obtained iris region is encoded using wavelets to construct the iris code.

As a result, a decision can be made in the matching step.

3.9 APPLICATION AREA

Iris recognition systems are being used today to control physical access, to facilitate identity verification and for computer authentication. Real world iris recognition applications have been implemented for airport and prison security, automatic teller machines (ATM), authentication using single sign-on, to replace ID cards, and to secure school and hospitals.

Application of iris recognition technology can he limited only by imagination. The important applications are the following:

3.9.1 ATM’S AND IRIS RECOGNITION: 

In U.S many banks incorporated iris recognition technology into ATM’s for the purpose of controlling access to one’s bank accounts. After enrolling once (a “30 second” process), the customer need only approach the ATM, follow the instruction to look at the camera, and be recognized within 2-4 seconds. The benefits of such a system are that the customer who chooses to use bank’s ATM with iris recognition will have a quicker, more secure transaction.

(Arun Ross, 2010)

Figure 8: ATM’s and Iris Recognition

3.9.2 TRACKING PRISONER MOVEMENT

The exceptionally high levels of accuracy provided by iris recognition technology broadens its applicability in high risk, high-security installations. Iris scan has implemented their devices with great success in prisons in Pennsylvania and Florida. By this any prison transfer or release is authorized through biometric identification. Such devices greatly ease logistical and staffing problems.

Applications of this type are well suited to iris recognition technology. First, being fairly large, iris recognition physical security devices are easily integrated into the mountable, sturdy apparatuses needed or access control, the technology’s phenomenal accuracy can be relied upon to prevent unauthorized release or transfer and to identify repeat offenders re-entering prison under a different identity.

( Hussein H. Fakhry and Benedict B. Cardozo, 2006)

Figure 9: Iris Recognition used in Prison

3.9.3 NATIONAL BORDER CONTROLS USING IRIS RECOGNITION

Iris scan has implemented their devices with great success in borders in Mexico and USA border. By this any person going to USA from Mexico is authorized through biometric identification. Such devices greatly ease logistical and staffing problems.

( Hussein H. Fakhry and Benedict B. Cardozo, 2006)

Figure 10: Iris Recognition used in Border

There are also used in the following areas:

Computer login: The iris as a living password.

Ticket less air travel.

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

Driving licenses and other personal certificates.

Entitlements and benefits authentication.

Forensics, birth certificates, tracking missing or wanted person 

Credit-card authentication.

Automobile ignition and unlocking; anti-theft devices.

Secure financial transaction (e-commerce, banking).

Internet security, control of access to privileged information. 

automobile ignition and unlocking; anti-theft devices 

secure access to bank accounts at cash machines 

 Internet security; control of access to privileged information

3.10 ADVANTAGES OF IRIS RECOGNITION

1. Highly protected, internal organ of the eye. A person’s iris is fully developed within 18 months after birth, and is protected by eyelashes, eyelids and the retina. This distinguishes it from fingerprints, which can be difficult to recognize after years of certain types of manual labour.

2. Externally visible pattern imaged from a distance

3. Patterns apparently stable throughout life

4. Iris shape is far more predictable than that of the face

5. No need for a person to touch any equipment

6. Its higher uniqueness in shape than face or fingerprints ensures that an authentication system using the iris is immensely reliable.

7. Iris recognition is proven the highest accuracy in biometrics. Iris recognition had no false matches

8. Iris patterns possess a high degree of randomness. Randomness in irises makes them very difficult to forge and hence imitate the actual person.

9. Scalability and speed of the technology are a major advantage.

10. Encoding and decision-making are tractable

11. Image analysis and encoding time: 1second

12. Search speed: 100000 Iris Codes per second

3.11 DISADVANTAGES OF IRIS RECOGNITION

1. Localization fails for dark iris

2. Highly prone for changes in weather or due to infection

3. The iris is a very small organ to scan from a distance. It can be obscured by eyelashes, lenses, reflections. Subjects who are blind or cataracts can also pose a challenge to iris recognition, as there is difficulty in reading the iris.

4. Equipment for scanning is expensive

5. 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.

CHAPTER FOUR

SUMMARY AND CONCLUSION

4.1 SUMMARY

The focus of this seminar research is on “Iris Recognition: A tool for modern security”. The study is justified for the simple reason that the Iris Recognition is an indispensable biometric for the growth of security in the world. To facilitate the attainment of the objectives of this study, the study was organized into four chapters. Chapter One involves the introduction, statement of the problems which consequently originated the study objectives. Accordingly, Chapter Two unravelled the theoretical framework for the study, while Chapter Three enumerated the Architecture of the study, component of the study, how it works the Application Area, Advantage and Disadvantage. The last chapter, being the fourth summarized, concluded and proffer recommendations.

4.2 CONCLUSION

The need for secure methods of authentication is becoming increasingly important in the corporate world today. Passwords, token cards and PINs are all risks to the security of an organisation due to human nature. Our inability to remember complex passwords and tendency to write these down along with losing token cards or forgetting PINs all contribute to the possible breakdown in security for an organisation.

The uniqueness of the iris and low probability of a false acceptance or false rejection all contribute to the benefits of using iris recognition technology. It provides an accurate and secure method of authenticating users onto company systems, is a non-intrusive method and has the speed required to minimise user frustration when accessing company systems. Users no longer have to worry about remembering passwords and system administrators no longer need to worry about the never-ending problem of users disclosing passwords or having weak passwords that are easily cracked. If a two-factor authentication system is implemented, for example iris recognition with a smart card, then the strength of authentication increases and provides another part to “defence in depth” for the company.

4.3 RECOMMENDATION

The following recommendation are general in scope and are intended for those institutions, security directors or managers of building, and homeland security experts with interest in access control devices.

· The effectiveness of Iris recognition as a physical access control measure can be improved by taking into account the architecture features of its environment. Police, Security experts and Criminologists have been aware of the role that the physical environment can play in security, crime and violence.

· Iris Recognition and / or appropriate housing devices should be develop and installed to protect against outdoor invaders or intruders.

· Overall, we found that Iris recognition technology can potentially be an effective way to control access during working hour if coupled with other less expensive and more frequently used security measures.

· If Iris Recognition technology is used in institution we recommend that institutions administrators and security personnel pay close attention to track and to include policies that will prevent doors from being propped open or unlock

· Iris Recognition can significantly reduce the work of front office staff if it is complemented by software designed to automatically print labels for each incoming visitor.

REFERENCES

Arun, R. (2010). Iris Recognition: The path forward. IEEE Computer Society, PP6-7.

Daugman, J. (2001). The Importance of Being Random: Statistical Principles of Iris Recognition. International Journal of Wavelet, Multi-resolution and Information Processing, PP3-6.

Daugman, J. and Downing, C. (2001). Epigenetic Randomness, Complexity and Singularity of Human Iris Patterns. Proceedings of the Royal Society: Biological Science, PP1737-1738.

Elgamal, M. (2013). An Efficient Feature Extraction Method for Iris Recognition Based on Wavelet Transformation. International Journal of Wavelets, Multi-resolution and Information Processing, PP10-11.

Ghatol, G. A. (2007). An Efficient Feature Extraction Method for Iris Recognition Based on Wavelet Transformation. Laser Focus World, PP64-65.

Hussein, H. and Benedict, B. (2006). Research and Development of an Iris-Based Recognition System for Identification and Secure Authentication. Information and Security, PP39-57.

Ives, Y. D. (2004). A New Approach to Iris Pattern Recognition. Boca Raton: FL: CRC Press.

John, D. (2003). The Importance of Being Random: Statistical Principles of Iris Recognition. Pattern Recogntion, PP1-17.

Penny, K. (2002). Iris Recognition Technology for Improved Authentication. SANS Institute, InfoSec Reading Room, P6.

Ramamoorthy, P. and Krishnamoorthy, R. (2012). Effective Iris Recognition For Security Enhancement. International Journal of Engineering Research and Applications (IJERA), PP1016-1019.

Ross, J. E. (2004). Effects of Segmentation Routine and Acquisition Environment on Iris Recognition. P1.

Vijay, D. et al. (2010). Bionmetric Recognition: A Modern Era for Security. International Journal of Engineering Science and Technology, P3364.

Yingzi, D. and Ives, R. (2004). A New Approach to Iris Pattern Recognition. SPIE Security and Defence, PP3-5.


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