The document is a thesis presentation on studying and developing iris segmentation and normalization techniques. It contains an introduction to biometrics and iris recognition. The document discusses literature on iris segmentation and normalization methods. It also covers topics like the anatomy and properties of the iris, existing iris recognition systems, and issues regarding biometrics. The goal is to develop an iris recognition system and evaluate its performance.

1. 1
Study and Development of Iris Segmentation & Normalization Technique
Pre – Submission Thesis Presentation
By : 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 of
Mr. Ashish Oberoi
Astt. Professor, Department of Comp. Sc. & Engg.
MMEC, M.M. University, Mullana
Submission : October 2011

2. 2
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

3. 3
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?

4. 4
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.

5. 5
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.

6. 6
Example Biometrics

7. 7
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.

8. 8
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.

9. 9
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.

10. 10
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.

11. 11
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.

12. 12
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

13. 13
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.

14. 14
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.

15. 15
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.

16. 16
Representation of an iris
Representation of
iris of a person
Textured region is
unique for a
person

17. 17
Iris Recognition
• Anatomy
• Some Interesting Facts about Iris
• Iris Recognition System
– Pros and Cons
• References

18. 18
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

19. 19
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.

20. 20
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.

21. 21
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.

22. 22
Iris Recognition System
(Acc. to Daugman’s Concept)
LocalizationAcquisition
IrisCode Gabor Filters Polar Representation
Image
Demarcated Zones

23. 23
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.

24. 24
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].

25. 25
Literature Survey
Iris Segmentation
• Daugman’s Method
Daugman [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 y
max r x y G r ds
r r
σ
π
∂
∗
∂ ∫Ñ
2
0
2
( )
2
1
( )
2
r r
G r e σ
σ
πσ
−
−
=

26. 26
Literature Survey
Iris Segmentation
• Wildes’ Method
Wildes [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 i
i
H x y r h x y x y r= ∑
0 0
0 0
1, ( , , , , ) 0( , , , , )
0,
i i
i i
if g x y x y rh x y x y r
otherwise
 == 

( , )i ix y i
2 2 2
0 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

27. 27
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 rxθ θ θ= − +
( , ) (1 ) ( ) ( )p iy r r y ryθ θ θ= − +
0( ) ( ) cos( )p p px x rθ θ θ= +
0( ) ( ) sin( )p p py y rθ θ θ= +
0( ) ( ) cos( )i i ix x rθ θ θ= +
0( ) ( ) sin( )i i iy y rθ θ θ= +
( ( ), ( ))p px yθ θ ( ( ), ( ))i ix yθ θ
0 0( ( ), ( ))p px yθ θ 0 0( ( ), ( ))i ix yθ θ

28. 28
Literature Survey…
Iris Normalization
Wildes’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 a
x y
errfn I x y I x u y v dxdy= − − −∫∫
'
'
( )
x x x
sR
y y y
φ
     
= − ÷  ÷  ÷
    

29. 29
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 0
2 2
0 0 0 0
( ) ( )
2 ( ) ( )
( , )
x x y y
i u x x v y y
G x y e e
π
πα β
 − −
− + 
− − + −  
=
2 2
0 0 0u vω = +
0 0( , )x y ( , )α β
0 0( , )u v

30. 30
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.
2
0
22
0
( )
( )
0
2
( )
( , )
r r
ii
e e e
H r
θ θω α
θ θ
θ
β
−
−
− −−
−
=
( , )α β 0 0( , )r θ
2 2 2 2
0 0 0( ) ( ) / ( ) /
{R ,I } sgn{R ,I } ( , ) .i r
e m e mh I e e e d dω θ φ ρ α θ φ β
ρ φ
ρ φ ρ ρ φ− − − − − −
= ∫∫
{R ,I }e mh
( , )I ρ φ

31. 31
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 i
i
HD X XOR Y
n =
= ∑
where X and Y are strings of n bits length.

32. 32
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=
1
2
2
1
( , ) ( )
p
i i
i
d x y x y
=
 
= − 
 
∑
( , ) ( ) ( )t
d x y x y x y= − −
• Normalized correlation is also used as classification metric.

33. 33
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.

34. 34
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.

35. 35
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 y
max r x y G r ds
r r
σ
π
∂
∗
∂ ∫Ñ
( , )* ( , )G x y I x y∇
( / , / )x y∇ ≡ ∂ ∂ ∂ ∂
2 2
0 0
2
( ) ( )
2
2
1
( , )
2
x x y y
G x y e σ
πσ
− − + −
=

36. 36
System Model & Implementation
Segmentation
• 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 c
j
H x y r h x y x y r
=
= ∑
,
,
1 ( , , , ) 0
( , , , )
0
j j c c
j j c c
if g x y x y r
h 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

37. 37
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 [0,2π].

38. 38
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 0
2 2
0 0 0 0
( ) ( )
2 ( ) ( )
( , )
x x y y
i u x x v y y
G x y e e
π
πα β
 − −
− + 
− − + −  
=
2 2 2 2
0 0 0( ) ( ) / ( ) /
{R ,I } sgn{R ,I } ( , ) .i r
e m e mh I e e e d dω θ φ ρ α θ φ β
ρ φ
ρ φ ρ ρ φ− − − − − −
= ∫∫
{R ,I }e mh
( , )I ρ φ

39. 39
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.

40. 40
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)

41. 41
Results
Histogram between Relative Frequency and Hamming Distance for Imposters
(Users 108, Sample 7)

42. 42
Results
Histogram between Relative Frequency and Hamming Distance for Genuine
(Users 108, Sample 7)

43. 43
Results
Histogram between Relative Frequency and Hamming Distance for Imposters &
Genuine both (Users 108, Sample 7)

44. 44
Results
ROC Curve between FAR and FRR for both genuine and imposters
(Users 108, Sample 7)

45. 45
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.

46. 46
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.

47. 47
References
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