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Study and Development of Iris Segmentation & Normalization Technique

Pre – Submission Thesis PresentationBy : Sunil Chawla (11077417)

SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENT FOR THE AWARD OF THE DEGREE OF

MASTER OF TECHNOLOGY(Information Technology)

Session – 2007-10

Under the guidance ofMr. Ashish Oberoi

Astt. Professor, Department of Comp. Sc. & Engg.MMEC, M.M. University, Mullana

Submission : October 2011

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Contents• Introduction to Biometrics• Iris• Introduction to Iris Recognition• Literature Survey• Problem Formulation• Objective of the Study• System Model & Implementation• Results• Future Scope and Conclusion• References

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Biometrics• What is Biometrics?

• What is the need of Biometrics?

• What is the current status of Biometrics?

• What are the issues need to be addressed in the area of Biometrics?

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Biometrics…• Biometrics is the reading of a unique human

physical attribute as data, which is then applied to actuating a system.

• Biometrics is the science and technology of measuring and analyzing biological data. In information technology, biometrics refers to technologies that measure and analyze human body characteristics, such as fingerprints, eye retinas and irises, voice patterns, facial patterns and hand measurements, for authentication purposes.

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Biometrics…• 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, hand and palm geometry, iris recognition, which has largely replaced retina, and odor/scent.

• Behavioral are related to the behavior of a person. Examples include, but are not limited to typing rhythm, gait, and voice. Some researchers[1] have coined the term behaviometrics for this class of biometrics.

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Example Biometrics

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Biometrics …It is possible to understand if a human characteristic can be used for biometrics in terms of the following parameters:

•Universality – each person should have that characteristic. •Uniqueness – is how well the biometric separates individuals from another. •Permanence – measures how well a biometric resists aging and other variance over time. •Collectability – ease of acquisition for measurement. •Performance – accuracy, speed, and robustness of technology used.•Acceptability – degree of approval of a technology. •Circumvention – ease of use of a substitute.

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Biometrics…• A biometric system can operate in the following two

modes:• 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.

• Identification – A one to many comparison 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.

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Need of Biometrics• History of identification of humans is as old as human beings.

• With the development in science and technology in the today’s modern world, human activities and transactions have been growing tremendously.

• Authentication can be defined as the process of determining whether a given person is indeed who he/she claims to be or not.

• Authenticity of users has become an inseparable part of all transactions involving human computer interaction.

• Most conventional modes of authentication are based on knowledge based systems i.e. “what we know” (e.g. passwords, PIN code etc) and / or token based systems i.e. “what we have” (e.g. ID cards, passports, driving license etc.).

• But knowledge based systems can be forgotten and token based systems can be lost. There occurs the need of Biometrics.

• A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual.

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Current Status of Biometrics• Currently Biometrics is in a non – mature state.• Many Challenges are still under research

– Performance– Capture Devices and Fraud Detection– Security– User reactions

• Various researchers are currently working on the betterment of Biometrics.

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Current Affairs of Biometrics in India• The Indian Finance Ministry has decided to issue

biometric PAN cards to taxpayers across the country to erase the problem of duplicate and fake ones. The decision was taken in the wake of a Comptroller and Auditor General (CAG) report that asked the Income Tax department to ensure that a single taxpayer is not issued multiple cards. The biometric PAN card will play an important role to stop the misuse of this vital identity document.

• India has launched an ambitious program to fit each of its 1.2 billion residents with an Unique identification number (UID). Each number will be tied into three pieces of biometric data: fingerprints (all ten digits), iris scans (both eyes), and a picture of the face.

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Key issues and concerns with biometrics:

• The main issues that need to be dealt with when biometrics is discussed are:

• False Rejection Rate and False Acceptance Rate

• Durability• Ease of use• Physical Privacy• Information Privacy• Religious objections

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Iris• The iris (plural: irides, or rarely, irises) is

a thin, circular structure in the eye, responsible for controlling the diameter and size of the pupils and thus the amount of light reaching the retina.

• The iris consists of two layers: the front pigmented fibro vascular tissue known as a stroma and, beneath the stroma, pigmented epithelial cells.

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Iris• The iris is divided into two major regions:• The pupillary zone is the inner region whose edge forms the

boundary of the pupil.• The ciliary zone is the rest of the iris that extends to its origin at

the ciliary body.

• Iris color is a highly complex phenomenon consisting of the combined effects of texture, pigmentation, fibrous tissue and blood vessels within the iris stroma, which together make up an individual's epigenetic constitution. 

• A person's "eye color" is actually the color of one's iris, the cornea being transparent and the white sclera entirely outside the area of interest. It is a common misconception that the iris color is entirely due to its melanin pigment; this varies only from brown to black.

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Iris….• Properties of the iris: • Has highly distinguishing texture.

– Right eye differs from left eye.– Probability of two same irises is almost zero.– Even Twins have different iris texture.– Not trivial to capture quality image.– Works well with cooperative subjects.– Used in many airports in the world.

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Representation of an iris

Representation of iris of a person

Textured region is unique for a person

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Iris Recognition

• Anatomy • Some Interesting Facts about Iris• Iris Recognition System

– Pros and Cons• References

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Anatomy of Iris• The iris is a circular and adjustable

diaphragm with the pupil. It is located in the chamber behind the cornea.

• The iris is the extension of a large and smooth muscle which also connects to the lens via a number of suspensor ligaments. These muscles expand and contract to change the shape of the lens and to adjust the focus of images onto the retina

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Some Interesting Facts about Iris• An iris pattern is absolutely unique. Not only iris patterns

of different individuals are different, but even a person’s left and right eyes also have completely unique iris patterns. It has been calculated that the chance of finding two randomly formed identical irises is on an almost astronomical order of 1 in 1078

• Iris patterns are formed at embryonic stage and continue developing till age 1, after which they are constant till death. The only exceptions would be accidents or surgery. This is another main advantage of iris as a biometric; almost every other biometric template changes significantly over time. This then necessitates frequent enrollment and also affects the system performance.

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Some Interesting Facts about Iris• Another important factor that makes iris technology

popular is the processing speed. Most iris recognition systems deliver 1-n searching of large databases in real time. This is the area in which iris is favored over fingerprint

• It has been proved that state of the art iris recognition systems capture about 249 degrees of freedom. Fingerprints, facial recognition and hand geometry do not provide as many details to help information of the template. This is one of the reason why iris recognition can authenticate with confidence even when significantly less than the whole eye is visible.

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Iris Recognition System• The iris recognition system consists of an automatic

segmentation system that is based on the edge detector and is able to localize the circular iris and pupil region, occluding eyelids, eyelashes and reflections.

• The extracted iris region is then normalized into a rectangular block with constant dimensions to account for imaging inconsistencies.

• Features are extracted with different feature extraction methods e.g. 1-D gabor filters to encode the unique pattern of the iris into biometric template.

• The Hamming distance was employed for classification of iris templates and two templates were found to match if hamming distance is greater than a specific threshold.

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Iris Recognition System(Acc. to Daugman’s Concept)

LocalizationAcquisition

IrisCode Gabor Filters Polar Representation

Image

Demarcated Zones

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Iris Recognition…

• Iris recognition is a method of biometric authentication that uses pattern recognition techniques based on images of the irises of an individual's eyes

• The work presented in this thesis involves developing an Iris Recognition System in order to verify both the uniqueness of the human iris and also its performance as a biometric.

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Literature Survey• The concept of automated iris recognition has been initially proposed by

Flom and Safir [1].

• Daugman [2] has used multi-scale quadrature wavelets to extract texture phase structure information of the iris to generate a 2048 bit iriscode and compared the difference between a pair of iris representations by computing their Hamming distance via the XOR operator.

• Boles and Boashash [3] have calculated zero-crossing representation of 1-D wavelet transform at various resolution levels of a virtual circle on an iris image to characterize the texture of the iris.

• Wildes et al. [4] have represented the iris texture with a Laplacian pyramid constructed with four different resolution levels and has used the normalized correlation to determine whether the input image and the model image are from the same class.

• An automatic segmentation algorithm based on the circular Hough transform is employed by Wildes et al. [4], Kong and Zhang [5], Tisse et al. [6], and Ma et al. [7].

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Literature SurveyIris Segmentation

• Daugman’s MethodDaugman [2] presented the first approach to computational iris recognition, including iris localization. An integro-differential operator is proposed for locating the inner and outer boundaries of an iris. The operator assumes that pupil and limbus are circular contours and performs as a circular edge detector. Integro-differential operator is defined as:

(1)

where I(x, y) is an image containing an eye. The integro-differential operator searches over the image domain (x, y) for the maximum in the blurred partial derivative with respect to increasing radius r of the normalized contour integral of I(x, y) along a circular arc ds of radius r and center coordinates (x0,y0). The symbol denotes convolution and Gσ(r) is a smoothing function such as a Gaussian of scale σ and is defined as:

(2)• The integro-differential operator behaves as a circular edge detector. It searches for the

gradient maxima over the 3D parameter space, so there are no threshold parameters required as in the Canny edge detector [8].

• Daugman simply excludes the upper and lower most portions of the image, where eyelid occlusion is expected to occur.

0 0

0 0, ,

( , ), , ( )2r x y

I x ymax r x y G r dsr r

202

( )21( )

2

r r

G r e

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Literature SurveyIris Segmentation

• Wildes’ MethodWildes [4] had proposed an iris recognition system in which iris localization is completed by detecting edges in iris images followed by use of a circular Hough transform [9] to localize iris boundaries. In a circular Hough transform, images are analyzed to estimate the three parameters of one circle using following equations:

(3)

Where is an edge pixel and is the index of the edge pixel

where, (4)

• The location with the maximum value of is chosen as the parameter vector for the strongest circular boundary. Wildes’ system models the eyelids as parabolic arcs. The upper and lower eyelids are detected by using a Hough transform based approach similar to that described above. The only difference is that it votes for parabolic arcs instead of circles.

0 0 0 0( , , ) ( , , , , )i ii

H x y r h x y x y r

0 00 0

1, ( , , , , ) 0( , , , , )0,

i ii i

if g x y x y rh x y x y rotherwise

( , )i ix y i

2 2 20 0 0 0( , , , , ) ( ) ( )i i i ig x y x y r x x y y r

0 0( , , )x y r 0 0( , , )H x y r

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Literature Survey…Iris Normalization

Daugman’s Method• Daugman’s system [2] uses radial scaling to compensate for overall size as well as a simple

model of pupil variation based on linear stretching. This scaling serves to map Cartesian image coordinates (x,y) to dimensionless polar coordinates (r,θ) according to the following equation

(5) (6)

where (7) (8) (9) (10)

• This model is called rubber sheet model which assumes that in radial direction, iris texture change linearly. This model maps the iris texture from pupil to iris outer boundary into the interval [0, 1] and θ is cyclic over [0,2π]. Here and are the coordinates of the iris inner and outer boundaries in the direction θ and and are the coordinates of pupil and iris centers respectively. Daugman compensates rotation invariance in matching process by circular shifting the normalized iris linearly in different directions.

( , ) (1 ) ( ) ( )p ix r r x rx ( , ) (1 ) ( ) ( )p iy r r y ry

0( ) ( ) cos( )p p px x r

0( ) ( ) sin( )p p py y r

0( ) ( ) cos( )i i ix x r

0( ) ( ) sin( )i i iy y r

( ( ), ( ))p px y ( ( ), ( ))i ix y

0 0( ( ), ( ))p px y 0 0( ( ), ( ))i ix y

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Literature Survey…

Iris NormalizationWildes’s Method• Wildes [4] has proposed a technique in which image is normalized to compensate

both scaling and rotation in matching step. This approach geometrically warps a newly acquired image Ia(x,y) into alignment with a selected database image Id(x,y) according to a mapping function (u(x, y), v(x, y)) such that for all the image intensity value at (x, y) − (u(x, y), v(x, y)) in Ia is close to that at (x, y) in Id.

• More precisely, the mapping function (u, v) is taken to minimize the following error function:

(11)

• Constrained is to capture a similarity transformation of image coordinates (x, y) to (x′, y′), i.e. (12)

Where s is scaling factor and R(ϕ) is a matrix representing rotation by ϕ.

2( ( , ) ( , ))d ax y

errfn I x y I x u y v dxdy

'

' ( )x x xsRy y y

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Literature Survey…

Feature Extraction and Encoding• Wavelets can be used to decompose the data in the iris

region into components that appear at different resolutions.

• A Gabor filter is constructed by modulating a sine/cosine wave with a Gaussian.

• Daugman makes uses of a 2D version of Gabor filters in order to encode iris pattern data.

• A 2D Gabor filter over the an image domain is represented as (13)

where specify position in image, represents the effective width and length, and specify modulation which has a spatial frequency

2 2

0 02 2

0 0 0 0

( ) ( )2 ( ) ( )( , )

x x y yi u x x v y yG x y e e

2 20 0 0u v

0 0( , )x y ( , )

0 0( , )u v

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Literature Survey

Feature Extraction and Encoding• This creates a compact 256-byte template, which allows for efficient

storage and comparison of irises. The Daugman system makes use of polar coordinates for normalization, therefore in polar form the filters are given as:

(14)

• where are the same as in equation (13) and specify the centre frequency of the filter. The demodulation and phase Quantization process can be represented as :

where can be regarded as a complex valued bit whose real and imaginary components are dependent on the sign of the 2D integral, and is the raw iris image in a dimensionless polar coordinate system.

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22 0

( )( )

02

( )( , )

r riie e e

H r

( , ) 0 0( , )r

2 2 2 20 0 0( ) ( ) / ( ) /{R , I } sgn{R , I } ( , ) .i r

e m e mh I e e e d d

{R , I }e mh

( , )I

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Literature Survey…Matching

• In iris recognition systems, the most widely used similarity metric is normalized Hamming distance

• In feature extraction module, if the features are converted in binary format then the Hamming distance is used to find the match. A threshold is defined regarding to normalized Hamming distance. Hamming distance less than the threshold value is assumed as match. The minimum the normalized Hamming distance, maximum is the matching factor. Normalized Hamming distance is defined as follows

1

1 ( )n

i ii

HD X XOR Yn

where X and Y are strings of n bits length.

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Literature Survey…Matching

• Euclidian distance is another similarity matrix which can be employed to compare the templates. Euclidean distance between two points in p-dimensional space is a geometrically shortest distance on the straight line passing through both the points. For a distance between two p-dimensional features and , the Euclidean distance metric is defined as

(15)

• In matrix notation, this is written as the following:

(16)

1 2( , ,... )px x x x 1 2( , ,... )py y y y

12

2

1

( , ) ( )p

i ii

d x y x y

( , ) ( ) ( )td x y x y x y

• Normalized correlation is also used as classification metric.

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Problem Formulation• Authentication and Identification of human being in Computer related

affairs as well as in daily life is becoming the need of hour.

• Biometrics provides a solid platform for replacing the traditional and obsolete measures of authentication like PINs, Passwords etc.

• Biometric Traits of human beings are able to work as passwords and these are less vulnerable to attacks as compared to the classic methods of authentication and security.

• Iris is one of the most unique, stable, universal, non-invasive and secure way of achieving the purpose.

• Iris recognition is in young age as the development started in early 1980s.

• The first Iris Recognition system was developed by J. Daugman in 1993 which is the most widely documented system available in open literature of research.

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Objective of the Study• To study about iris anatomy, different techniques of iris segmentation,

normalization, feature extraction and comparison.

• Segmentation (locating the iris region in an eye image) is to be done by implementing Daugman’s integro - differential equation and Hough transform.

• Normalization (creating a dimensionally consistent representation of the iris region) is to be done by using Daugman’s rubber sheet model.

• Feature encoding (creating a template containing only the most discriminating features of the iris), is to be done using one-dimensional log-Gabor filters & Matching is to be performed using Hamming distance similarity matrix.

• To implement prominent iris recognition algorithms in MATLAB®. The system is to be composed of a number of sub-systems, which correspond to each stage of iris recognition. 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.

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System Model & Implementation• The first stage of iris recognition is to isolate the actual iris region in

a digital eye image. This process is known as Image Segmentation.

• Integro - differential operator is used for localizing iris, pupil and sclera.

• A nonlinear enhancement of this operator makes it more robust for detecting the inner boundary of the iris. Hough transform is based on the first derivative of the image.

• An edge map of the image is first obtained by thresholding the magnitude of the image intensity gradient:

where and is a Gaussian smoothing function with scaling parameter to select the proper scale of edge analysis.

0 0

0 0, ,

( , ), , ( )2r x y

I x ymax r x y G r dsr r

( , )* ( , )G x y I x y

( / , / )x y 2 2

0 02

( ) ( )2

2

1( , )2

x x y y

G x y e

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System Model & ImplementationSegmentation

• The edge map is then used in a voting process to maximize the defined Hough transform for the desired contour. Considering the obtained edge points as, a Hough transform can be written as:

where

• The limbus and pupil are both modeled as circles and the parametric function g is defined as:

• Assuming a circle with the center and radius r the edge points that are located over the circle result in a zero value of the function.

• The value of g is then transformed to 1 by the h function, which represents the local pattern of the contour. The local patterns are then used in a voting procedure using the Hough transform, in order to locate the proper pupil and limbus boundaries.

,1

( , , ) ( , , , )n

c c j j c cj

H x y r h x y x y r

,,

1 ( , , , ) 0( , , , )

0j j c c

j j c c

if g x y x y rh x y x y r

otherwise

2 2 2,( , , , ) ( ) ( )j j c c j c j cg x y x y r x x y y r

( , )c cx y

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System Model & Implementation

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 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.

• Daugman’s Rubber Sheet Model

• The rubber sheet model assigns to each point on the iris, regardless of its size and pupillary dilation, a pair of real coordinates, where r is on the unit interval [0, 1] and θ is an angle in [0,2].

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System Model & Implementation

Feature Extraction, Encoding and Matching

• A 2D version of Gabor filters in order to encode iris pattern data. A 2D Gabor filter over the an image domain is represented as

• Decomposition of a signal is accomplished using a quadrature pair of Gabor filters, with a real part specified by a cosine modulated by a Gaussian, and an imaginary part specified by a sine modulated and odd symmetric components respectively.

• where can be regarded as a complex valued bit whose real and imaginary components are dependent on the sign of the 2D integral,

• is the raw iris image in a dimensionless polar coordinate system.

2 2

0 02 2

0 0 0 0

( ) ( )2 ( ) ( )( , )

x x y yi u x x v y yG x y e e

2 2 2 20 0 0( ) ( ) / ( ) /{R , I } sgn{R , I } ( , ) .i r

e m e mh I e e e d d

{R , I }e mh

( , )I

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Results• Results are calculated for CASIA Version 1

Iris Image Database (Users 108, Sample 7).• distance_1 = 3.6892• hd (avg. hamming distance)= 0.3211• Elapsed time is 8361.895547 seconds.

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Results

Histogram between Relative Frequency and Hamming Distance for Imposters, Genuine, Both Imposter and genuine and ROC Curve between FRR and FAR for both Genuine

and Imposters (Users 108, Sample 7)

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Results

Histogram between Relative Frequency and Hamming Distance for Imposters (Users 108, Sample 7)

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Results

Histogram between Relative Frequency and Hamming Distance for Genuine (Users 108, Sample 7)

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Results

Histogram between Relative Frequency and Hamming Distance for Imposters & Genuine both (Users 108, Sample 7)

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Results

ROC Curve between FAR and FRR for both genuine and imposters (Users 108, Sample 7)

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Conclusion• In this thesis Daugman’s and Wilde’s algorithms were implemented in order

to evaluate the impact of different segmentation and normalization methods on the overall performance, 756 iris images from CASIA iris image Database were used in the experiments.

• Daugman’s method has one important advantage: it is not dependent of any parametric value. This fact may, in theory, potentiate its robustness, but the results showed that its accuracy is influenced by the images quality, namely the requirements of a sufficient separability between the intensities of the iris and sclera regions.

• Specially in the iris images with higher intensity values, where the intensity difference between the iris and sclera regions is not as large, the method’s seek strategy for the maximal difference between consecutive circumferences tends to identify regions tangent to the pupil region, which have considerable high contrast.

• Eyelids and eyelashes occlusion also degraded segmentation using Daugman’s and Wildes’s method.

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Future Scope• Noise Factors like occlusion of Eyelashes, Eyelids, specular reflection,

illumination of light, Occlusion of Spectacles etc. can be dealt with great details in Future Work.

• The Algorithm can be implemented on UBIRIS[10] and MMU[11] iris databases for more detailed comparison.

• A lots of different Wavelets are available in Digital Image Processing, which can be employed in place of Gabor Wavelet in the proposed approach which in turn may give better results.

• More Comparative studies on the under discussion work can result better performances and better recognition rates.

• Recent Trends in Iris Recognition shows the inclination of researchers in favour of Iris Recognition in less constrained and less helpful environment where noise factors are freely available and are considered and dealt with more emphasis.

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References• [1] L. Flom and A. Safir, "Iris recognition system," U.S. Patent 4 641 349,

1987.• [2] J. Daugman, "Biometric Personal Identification System Based on Iris

Analysis," US patent 5 291 560, 1994.• [3] W. Boles and B. Boashash, "A Human Identification Technique Using

Images of the Iris and Wavelet Transform," IEEE Trans. Signal Processing, vol. 46, pp. 1185-1188, 1998.

• [4] R. Wildes, "Iris recognition: an emerging biometric technology," Proceedings of the IEEE, vol. 85, pp. 1348-1363, 1997.

• [5] W. K. Kong and D. Zhang, “Accurate iris segmentation method based on novel reflection and eyelash detection model,” Proceedings of the International Symposium on Intelligent Multimedia, Video and Speech Processing, pp. 263–266, Hong Kong, May 2001.

• [6] C. Tisse, L. Martin, L. Torres, and M. Robert, “Person identification technique using human iris recognition,” Proceedings of the 25th International Conference on Vision Interface, pp. 294–299, Calgary, July 2002.

• [7] L. Ma, T. Tan, Y. Wang, and D. Zhang, “Personal identification based on iris texture analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 12, pp. 2519–2533, December 2003.

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References• [8] J. Canny, "A Computational Approach to Edge Detection," IEEE

Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-8, pp. 679-698, 1986.

• [9] "Hough Transform”,http://en.wikipedia.org/wiki/Hough_transform accessed 2010.

• [10] H. Proenca and L. A. Alexandre, "Ubiris: A noisy iris image database," in 13th International Conference on Image Analysis and Processing, 2005, pp. 970-977.

• [11] "Multimedia University, Iris database," http://persona.mmu.edu.my/~ accessed, 2009.

• [12] L. Masek and P. Kovesi, MATLAB source code for a biometric identification system based on iris patterns, The University of Western Australia, http://www.csse.uwa.edu.au/~pk/studentprojects/libor/, 2003.

• [13] J. G. Daugman, “High confidence visual recognition of persons by a test of statistical independence,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 11, pp. 1148–1161, November 1993.

• [14] Institute of Automation, Chinese Academy of Sciences, CASIA iris image database, http://www.sinobiometrics.com, 2010.

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References• [15] J. Daugman, "Biometric Personal Identification System Based on Iris

Analysis," US patent 5 291 560, 1994.• [16] R. C. Gonzalez and R. E. Woods, Digital Image Processing, Second ed:

Prentice Hall, Upper Saddle River, New Jersey, 2002.• [17] J. Daugman, "How iris recognition works," IEEE Transactions on Circuits

and Systems for Video Technology, vol. 14, pp. 21-30, 2004.

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