FINAL REPORT

IRIS RECOGNITION 2010 IRIS RECOGNITION By Abhay Kr. Goswami

(0716113001)

Akaanksha Singh

(0716131006)

Anamika

(2816113001)

Submitted to the Department of Information Technology in partial fulfillment of the requirements for the degree of Bachelor of Technology In Information Technology

Krishna Engineering College Uttar Pradesh Technical University (2010-2011)

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IRIS RECOGNITION 2010

CERTIFICATE This is to certify that Project Report entitled “IRIS RECOGNITION” which is submitted in partial fulfillment of the requirement for the award of degree B. Tech. in Department of Information Technology of U. P. Technical University, is a record of the candidate own work carried out by him under my/our supervision. The matter embodied in this thesis is original and has not been submitted for the award of any other degree.

(Mr. Vivek Verma) Deptt CS/IT

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IRIS RECOGNITION 2010 CERTIFICATE

This is to certify that Project Report entitled “IRIS RECOGNITION” which is submitted by Abhay Kr. Goswami (0716113001) in partial fulfillment of the requirement for the award of degree B. Tech. in Department of Information Technology of U. P. Technical University, is a record of the candidate own work carried out by him under my/our supervision. The matter embodied in this thesis is original and has not been submitted for the award of any other degree.

Date: Supervisor Mrs. Sai Sabitha

CERTIFICATE 3 |Page

IRIS RECOGNITION 2010

This is to certify that Project Report entitled “IRIS RECOGNITION” which is submitted by Akaanksha Singh (0716131006) in partial fulfillment of the requirement for the award of degree B. Tech. in Department of Information Technology of U. P. Technical University, is a record of the candidate own work carried out by him under my/our supervision. The matter embodied in this thesis is original and has not been submitted for the award of any other degree.

Date: Supervisor Mrs. Sai Sabitha

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IRIS RECOGNITION 2010 CERTIFICATE

This is to certify that Project Report entitled “IRIS RECOGNITION” which is submitted by Anamika (2816113001) in partial fulfillment of the requirement for the award of degree B. Tech. in Department of Information Technology of U. P. Technical University, is a record of the candidate own work carried out by him under my/our supervision. The matter embodied in this thesis is original and has not been submitted for the award of any other degree.

Date: Supervisor Mrs. Sai Sabitha

DECLARATION 5 |Page

IRIS RECOGNITION 2010

I hereby declare that this submission is my own work and that, to the best of my knowledge and belief, it contains no material previously published or written by another person nor material which to a substantial extent has been accepted for the award of any other degree or diploma of the university or other institute of higher learning, except where due acknowledgment has been made in the text.

Signature Name: Abhay Kr. Goswami Roll No.: 0716113001 Date:

Signature Name: Akaanksha Singh Roll No.: 0716131006 Date:

Signature Name: Anamika Roll No.: 28716113001 Date:

ABSTRACT 6 |Page

IRIS RECOGNITION 2010

A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Iris recognition is regarded as the most reliable and accurate biometric identification system available. Most commercial iris recognition systems use patented algorithms developed by Daugman, and these algorithms are able to produce perfect recognition rates. However, published results have usually been produced under favourable conditions, and there have been no independent trials of the technology. The work presented in this thesis involved developing an ‘Open-Source’ Iris Recognition System in order to verify both the uniqueness of the human iris and also its performance as a biometric. For determining the recognition performance of the system two databases of digitized grayscale eye images were used. The iris recognition system consists of an automatic segmentation system that is based on the Hough transform, and is able to localize the circular iris and pupil region, occluding eyelids and eyelashes, and reflections. The extracted iris region was then normalized into a rectangular block with constant dimensions to account for imaging inconsistencies. Finally, the phase data from 1D Log-Gabor filters was extracted and quantized to four levels to encode the unique pattern of the iris into a bit-wise biometric template. The Hamming distance was employed for classification of iris templates, and two templates were found to match if a test of statistical independence was failed. The system performed with perfect recognition on a set of 75 eye images; however, tests on another set of 624 images resulted in false accept and false reject rates of 0.005% and 0.238% respectively. Therefore, iris recognition is shown to be a reliable and accurate biometric technology.

ACKNOWLEDGEMENT

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IRIS RECOGNITION 2010 It gives us a great sense of pleasure to present the report of the B. Tech Project undertaken during B. Tech. Final Year. We owe special debt of gratitude to Mrs. Sai Sabitha, Department of Information Technology, Krishna Engineering College, and Ghaziabad for her constant support and guidance throughout the course of our work. Her sincerity, thoroughness and perseverance have been a constant source of inspiration for us. It is only her cognizant efforts that our endeavors have seen light of the day. We also take the opportunity to acknowledge the contribution of Mr. Vivek Verma, Department of Computer Science & Engineering / Information Technology, Krishna Engineering College, Ghaziabad for his full support and assistance during the development of the project. We also do not like to miss the opportunity to acknowledge the contribution of all faculty members of the department for their kind assistance and cooperation during the development of our project. Last but not the least, we acknowledge our friends for their contribution in the completion of the project.

CONTENTS

ABSTRACT.............................................................................................II 8 |Page

IRIS RECOGNITION 2010 ACKNOWLEDGEMENTS....................................................................III CONTENTS..............................................................................................IV

CHAPTER 1 ……………..……………………………………………… INTRODUCTION…….…………………………………………………. 1.1 Biometric Technology………………………………………………………..…. 1.2 The Human Iris…………………………………………………………………. 1.3 Objective…………………………………………………………………………. 1.4 Purpose………………………………………………………………………….. CHAPTER 2……………………………………………………………………….. LITERATURE SURVEY…………………………………………………………. 2.1 What is Iris Recognition? ……………………………………………………………………… 2.2 Methodologies………………………………………………………………………………… …. 2.3 Technology Used…………………………………………………………………………………….. CHAPTER 3……………………………………………………………………………… MODULES…………………………………………………………………………………. 3.1 Image Acquisition………………………………………………………………………………………. 3.2 Preprocessing………………………………………………………………………………… …………….. 3.3 Feature Extraction……………………………………………………………………………………….

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IRIS RECOGNITION 2010 3.4 Recognition……………………………………………………………………………………… ……….

CHAPTER 4……………………………………………………………………….. DESIGN…………………………………………………………………………….. 4.1 Overall Structure………………………………………………………… 4.2 Snapshots………………………………………………………………….. CHAPTER 5………………………………………………………………………. CONCLUSION…………………………………………………………………….. CHAPTER 6………………………………………………………………………… FUTURE PROSPECTS………………………………………………………………….. CHAPTER 7……………………………………………………………………………. REFRENCES………………………………………………………………………………..

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IRIS RECOGNITION 2010

CHAPTER 1 INTRODUCTION

1.1 Biometric Technology Biometrics comprises methods for uniquely recognizing humans based upon one or more intrinsic physical or behavioural traits. In Computer Science, in particular, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. Biometric characteristics can be divided in two main classes: • Physiological are related to the shape of the body. Examples include, but are not limited

to fingerprint, face recognition, DNA, Palm print, hand geometry, iris recognition, which has largely replaced retina, and odour/scent. • Behavioral are related to the behavior of a person. Examples include, but are not limited

to typing rhythm, gait, and voice. Some researchers have coined the term behaviometrics for this class of biometrics. Today, biometric recognition is a common and reliable way to authenticate the identity of a living person based on physiological or behavioural characteristics. A physiological characteristic is relatively stable physical characteristics, such as fingerprint, iris pattern, facial feature, hand silhouette, etc. This kind of measurement is basically unchanging and unalterable without significant duress. A behavioural characteristic is more a reflection of an individual’s psychological makeup as signature, speech pattern, or how one types at a keyboard. The degree of intra-personal variation in a physical characteristic is smaller than a behavioural characteristic Nevertheless, all physiology-based biometrics don’t offer satisfactory recognition rates (false acceptance and/or false reject rates, respectively referenced as FAR and FRR). The automated personal identity authentication systems

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IRIS RECOGNITION 2010 Based on iris recognition are reputed to be the most reliable among all biometric methods: we consider that the probability of finding two people with identical iris pattern is almost zero. That’s why iris recognition technology is becoming an important biometric solution for people identification in access control as networked access to computer application. Compared to fingerprint, Iris is protected from the external environment behind the cornea and the eyelid. No subject to deleterious effects of aging, the small-scale radial features of the iris remain stable and fixed from about one year of age throughout life. Biometric systems work by first capturing a sample of the feature, such as recording a digital sound signal for voice recognition, or taking a digital color image for face recognition. The sample is then transformed using some sort of mathematical function into a biometric template. The biometric template will provide a normalized, efficient and highly discriminating representation of the feature, which can then be objectively compared with other templates in order to determine identity. Most biometric systems allow two modes of operation. An enrolment mode for adding templates to a database, and an identification mode, where a template is created for an individual and then a match is searched for in the database of pre-enrolled templates.

Fig 1.1 The basic block diagram of a biometric system 12 | P a g e

IRIS RECOGNITION 2010 A good biometric is characterized by use of a feature that is; highly unique – so that the chance of any two people having the same characteristic will be minimal, stable – so that the feature does not change over time, and be easily captured – in order to provide convenience to the user, and prevent misrepresentation of the feature. The purpose of ‘Iris Recognition’, a biometrical based technology for personal identification and verification, is to recognize a person from his/her iris prints. In fact, iris patterns are characterized by high level of stability and distinctiveness. Each individual has a unique iris; the difference even exists between identical twins and between the left and right eye of the same person. We implemented ‘Iris Recognition’ using MATLAB for its ease in image manipulation and wavelet applications.

1.2 The Human Iris The iris is a thin circular diaphragm, which lies between the cornea and the lens of the human eye. A front-on view of the iris is shown in Figure 1.1. The iris is perforated close to its centre by a circular aperture known as the pupil. The function of the iris is to control the amount of light entering through the pupil, and this is done by the sphincter and the dilator muscles, which adjust the size of the pupil. The average diameter of the iris is 12 mm, and the pupil size can vary from 10% to 80% of the iris diameter. The iris consists of a number of layers; the lowest is the epithelium layer, which contains dense pigmentation cells. The stromal layer lies above the epithelium layer, and contains blood vessels, pigment cells and the two iris muscles. The density of stromal pigmentation determines the color of the iris. The externally visible surface of the multi-layered iris contains two zones, which often differ in color. An outer ciliary zone and an inner papillary zone, and these two zones are divided by the collarette – which appears as a zigzag pattern.

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IRIS RECOGNITION 2010

Figure 1.2 Example of an iris pattern, imaged monochromatically at a distance of about 35 cm. The outline overlay shows results of the iris and pupil localization and eyelid detection steps.

Although small (11 mm) and sometimes problematic to image, the iris has the great mathematical advantage that its pattern variability among different persons is enormous. In addition, as an internal (yet externally visible) organ of the eye, the iris is well protected from the environment, and stable over-time. As a planar object its image is relatively insensitive to angle of illumination, and changes in viewing angle cause only af_ne transformations; even the nonaf_ne pattern distortion caused by pupillary dilation is readily reversible. Finally, the ease of localizing eyes in faces, and the distinctive annular shape of the iris, facilitates reliable and precise isolation of this feature and the creation of a size-invariant representation. The iris begins to form in the third month of gestation and the structures creating its pattern are largely complete by the eighth month, although pigment accretion can continue into the postnatal years. Its complex pattern can contain many distinctive features such as arching ligaments, furrows, ridges, crypts, rings, corona, freckles, and a zigzag collarette, some of which may be seen in Figure 1.2. Iris colour is determined mainly by the density of melanin pigment in its anterior layer and stroma. The striated trabecular meshwork of elastic pectinate ligament creates the predominant texture under visible light, whereas in the near infrared (NIR) wavelengths used for unobtrusive imaging at distances of up to 1 meter, deeper and somewhat more slowly modulated stromal features dominate the iris pattern. In NIR wavelengths, even darkly pigmented irises reveal rich and complex features.

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IRIS RECOGNITION 2010 1.3 Objective The objective will be to implement an open-source iris recognition system in order to verify the claimed performance of the technology. The development tool used will be MATLAB, and emphasis will be only on the software for performing recognition, and not hardware for capturing an eye image. A rapid application development (RAD) approach will be employed in order to produce results quickly. MATLAB provides an excellent RAD environment, with its image processing toolbox, and high level programming methodology. The system is to be composed of a number of sub-systems, which correspond to each stage of iris recognition. These stages are segmentation – locating the iris region in an eye image, normalization – creating a dimensionally consistent representation of the iris region, and feature encoding – creating a template containing only the most discriminating features of the iris. The input to the system will be an eye image, and the output will be an iris template, which will provide a mathematical representation of the iris region.

1.4 PURPOSE

The basic reason for such an approach is to attain a system that would ensure security thereby check for the authentication of the user. Since, Iris for every individual is unique therefore using this concept in the making of Iris Recognition System where your iris and its binary code would act as your password to allow the access. This system constitutes to be the most promising and efficient in addition to its accuracy among all the facilities of Biometrics. The use of Iris Recognition System has also been introduced in Army for checking of authentication of the intruders and the authenticated officers.

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IRIS RECOGNITION 2010 CHAPTER 2 EXHAUSTIVE LITERATURE SURVEY 2.1 What is Iris Recognition? The iris is an externally visible, yet protected organ whose unique epigenetic pattern remains stable throughout adult life. These characteristics make it very attractive for use as a biometric for identifying individuals. Image processing techniques can be employed to extract the unique iris pattern from a digitised image of the eye, and encode it into a biometric template, which can be stored in a database. This biometric template contains an objective mathematical representation of the unique information stored in the iris, and allows comparisons to be made between templates. When a subject wishes to be identified by an iris recognition system, their eye is first photographed, and then a template created for their iris region. This template is then compared with the other templates stored in a database until either a matching template is found and the subject is identified, or no match is found and the subject remains unidentified.

2.2 Methodologies: Boles and Boashash, Lim et al., and Noh et al. The algorithms by Lim et al. are used in the iris recognition system developed by the Ever media and Senex companies. These are, apart from the Daugman system, the only other known commercial implementations. The Daugman system has been tested under numerous studies, all reporting a zero failure rate. The Daugman system is claimed to be able to perfectly identify an individual, given millions of possibilities. The prototype system by Wildes et al. also reports flawless performance with 520 iris images, and the Lim et al. system attains a recognition rate of 98.4% with a database of around 6,000 eye images. Compared with other biometric technologies, such as face, speech and finger recognition, iris recognition can easily be considered as the most reliable form of biometric technology. However, there have been no independent trials of the technology, and source code for systems is not available. Also, there is a lack of publicly available datasets for testing and research, and the test results published have usually been produced using carefully imaged irises under favorable conditions.

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IRIS RECOGNITION 2010 2.3 Technology Used

2.3.1 Software Interface APPLICATION USED

: MATLAB

OPERATING SYSTEM

:

Window 7

2.3.2 Hardware Interface PROCESSOR

: PENTIUM IV 2.6 GHz

RAM

: 512 MB

HARD DISK

: 20 GB

CHAPTER 3 MODULES 17 | P a g e

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The system, as shown in Figure, is implemented in MATLAB. A general iris recognition system is composed of four steps. Firstly an image containing the eye is captured then image is preprocessed to extract the iris. Thirdly Eigen irises are used to train the system and finally decision is made by means of matching.

3. I IMAGE ACQUISITION

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IRIS RECOGNITION 2010 In iris recognition image acquisition is an important step. Since iris is small in size and dark in color, it is difficult to acquire good image. Also all the subsequent steps depend on it. A lenovo camera has been used to take eye snaps while trying to maintain appropriate settings such as lighting, distance to the camera and resolution of the image. The image is then changed from RGB to gray level for further processing.

3.2 PREPROCESSING First of all to separate the iris from the image the boundaries of the iris and pupil are detected. Since pupil is the darkest area in the image as shown in Figure; so a rough estimate of its center (Cx, Cy) is performed using the following formula from center of the pupil to the boundary between iris and pupil on different.

.

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IRIS RECOGNITION 2010

Direction n in the binary image and then averaging them. To detect the boundary between iris and sclera [6], the image is convolved with a blurring function which is a 2D Gaussian operator with center at (X0, Y0 )

where σ is standard deviation that smoothes the image and then apply Canny operator with the threshold values 0.005 and 0.1 as lower and upper limits. Now image is binarized to find the radius of iris with similar way just as for pupil. These two radii localize the iris then this hollow disk is mapped to a rectangle using following formula:

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IRIS RECOGNITION 2010

where r lies on the unit interval [0, 1] and θ is circular angle in [0, 2π ). This unwrapping is started from inner to outer boundary of iris, m concentric circles are obtained, then n samples are collected on each concentric circle, so m* n matrix represents the specific flat iris. Every sample started from vertical downward line in the counter clockwise direction.

3.2(A) Iris localization

Before performing iris pattern matching, the boundaries of the iris should be located. In other words, we are supposed to detect the part of the image that extends from inside the limbus (the border between the sclera and the iris) to the outside of the pupil. We start by determining the outer edge by first down sampling the images by a factor of 4, to enable a faster processing delay, using a Gaussian Pyramid. We then use the canny operator with the default threshold value given by MATLAB, to obtain the gradient image. Next, we apply a Circular summation which consists of summing the intensities over all circles, by using three nested loops to pass over all possible radii and center coordinates. The circle with the biggest radius and highest summation corresponds to the outer boundary. The center and radius of the iris in the original image are determined by rescaling the obtained results. After having located the outer edge, we next need to find the inner one which is difficult because it is not quite discernable by the Canny Operator especially for darkened people. Therefore, after detecting the outer boundary, we test the intensity of the pixels within the iris. Depending on this intensity, the threshold of the Canny is chosen. If the iris is dark, a low threshold is used to enable the canny operator to mark out the inner circle separating the iris from the pupil. If the iris is light colored, such as blue or green, then a higher threshold is utilized. The pupil center is shifted by up to 15% from the center of the iris and its radius is not greater than 0.8 Neither lowers than 0.1 of the radius of the iris. This means that processing time, dedicated to the search of the center of the pupil of this part is relatively small. Hence, instead of searching a down sample version of the iris, we searched the original one to gain maximum accuracy. Thus we have determined the boundaries of the iris as shown in following figure and we can then manipulate this zone to characterize each eye. 21 | P a g e

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3.3 FEATURE EXTRACTION The developed system has been trained to four irises of each class. In the training process mean of trained irises is subtracted from each iris.

where k is the total number of irises and Ii is the ith iris image. Eigen vectors are calculated for the outer product of the each iris. Thus, Eigen vector corresponding to the highest Eigen value is used as distinctive feature of the iris.

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IRIS RECOGNITION 2010 3.4 RECOGNITION

Euclidean distance is used as a classifier of an unknown testing iris and is compared with a set of known iris images. It is identified as iris “x” where with the iris “x” it has minimum Euclidian distance.

CHAPTER 4 DESIGN 4.1 STRUCTURE OF SYSTEM 23 | P a g e

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To easily manipulate the images in our database we built an interface that allows the user to choose between different options. The first one is to select two images to compare. The second allows the verification of the correspondence between the name entered and a chosen eye image. The third option is to identify the person through his/her eye. The iris recognition software that we implemented (Figure 7) is used to secure these three options. The flow chart in Figure 8 shows in detail how the interface we built operates.

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4.2 SNAPSHOTS

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CHAPTER 5 CONCLUSION

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We have successfully developed a system capable of comparing two digital eye-images. The image would be compared with the one stored accessed from database. The GUI would contain web-cam that would be used to capture the input image. The errors that occurred can be easily overcome by the use of stable equipment.

CHAPTER 6 FUTURE PROSPECTS

This identification system is quite simple requiring few components and is effective enough to be integrated within security systems that require an identity check. Judging by the clear 27 | P a g e

IRIS RECOGNITION 2010 distinctiveness of the iris patterns we can expect iris recognition systems to become the leading technology in identity verification. We look forward for the completion of the remaining modules and database connectivity in the coming semester thereby completing the overall Iris recognition System.

CHAPTER 7 REFERENCES

1) Daugman, J. 2004. How iris recognition works. IEEE Trans, CSVT 14, 21—30 28 | P a g e

IRIS RECOGNITION 2010 2) Daugman, J. The importance of being random: Statistical principles of iris recognition. Pattern Recognition, vol. 36, num. 2, pp. 279-291, 2003 3) Daugman, J. “How Iris Recognition Works”, available at: http://www.ncits.org/tc_home/m1htm/docs/m1020044.pdf.

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