Study and Implementation Of Iris

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Nov 30, 2013 (3 years and 11 months ago)

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Study

and Implementation

Of Iris
Recognition

Schemes




By:Ritika Jain

ritika.jain@mavs.uta.edu

Under guidance of

DR K R RAO

UNIVERSITY OF TEXAS AT ARLINGTON

SPRING 2012

P
roposal

This project is focussed upon studying and implementing
the various iris recognition schemes available and an
analysis of the different algorithms using Chinese
academy of sciences institute of automation (CASIA)
[14] database.


A
n
Introduction

[19]


Biometric technology is widely used for personnel
identity identification.

Typical biometric technologies include fingerprint
identification, face recognition,iris recognition etc.
Iris recognition is regarded as the most reliable
and accurate biometric identification system
available
.

Iris recognition is a highly efficient technology
used as an identification system.

General working of biometric
systems [3]


A
biometric system first captures the sample of the
feature which is then transformed using some sort
of mathematical function

into a biometric template
and this 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 namely enrolment and identification.


Iris recognition is an automated method of
biometric
identification
that uses mathematical
pattern
-
recognition techniques on video images of
the
irides

of
an
individual's

eyes
,
whose complex
random patterns are unique and can be seen from
some distance.



Comparison of iris recognition and
retinal scanning [19]:


Iris
Recognition uses a camera which is similar to
that in a home video camcorder to capture an
image of the Iris. A picture is taken from a distance
of 3 to 10 inches away
. Iris
recognition uses
camera technology with subtle
infrared
illumination to acquire images of iris.

While in case of retinal scanning, a very close
encounter with a scanning device is required, that
sends a beam of light deep inside the eye to
capture an image of the retina. (intrusive process
required to capture an image).













Masek's
Principle [3]


The iris recognition system consists of :



automatic segmentation system that is based

on the
Hough transform, and is able to localize the circular iris
and pupil regions



The extracted iris region is then

normalized into a
rectangular block with constant dimensions to account
for imaging

inconsistencies.





Finally, the phase data from 1D Log
-
Gabor
filters is extracted and

quantized to four levels to
encode the unique pattern of the iris into a bit
-
wise

biometric template.




The Hamming distance [3] is employed for
classification of iris templates, and two

templates
are found to match if a test of statistical
independence has failed.


The input to the system is an eye image, and

the
output is an iris template, which will provide a
mathematical representation

of the iris region.



Types of segmentation techniques
available [
3
]



Hough transform (employed by
Wildes

et al,
[7])


Daugman’s

integro
-
differential operator
approach, [5]


Active contour models (used by Ritter, [17])


Eyelash and noise detection (used by Kong
and Zhang, [16])

Segmentation technique in Masek's
method [3]

Segmentation results in localizing the iris region from an
eye image and isolating eyelid, eyelash and reflection
areas. Hough transform is used which first involves
Canny edge [10] detection to generate edge map using
Kovesi's Canny
edge detection MATLAB function [10]

Eyelids detection is done using Hough transform
using

[10], [11]

MATLAB functions involved in
segmentation technique [3], [10], [11]


createiristemplate
-

generates a biometric template
from an iris

eye image.


segmentiris
-

peforms automatic segmentation of the
iris region

from an eye image. Also isolates noise areas
such as occluding

eyelids and eyelashes.


addcircle
-

circle generator for adding weights into a
Hough accumulator

array.


adjgamma
-

for adjusting image gamma.


circlecoords
-

returns the pixel coordinates of a
circle defined by the

radius and x, y coordinates of
its center.


CANNY
-

Canny edge detection
-

function to
perform Canny edge detection.


findcircle
-

returns the coordinates of a circle in
an image using the Hough transform

and Canny
edge detection to create the edge map.





findline
-

returns the coordinates of a line in an image
using the Hough transform and Canny edge detection to
create

the edge map.


houghcircle
-

takes an edge map image, and performs
the Hough transform

for finding circles in the image.


HYSTHRESH
-

Hysteresis thresholding
-

Function
performs hysteresis thresholding of an image
.


linecoords
-

returns the x y coordinates of
positions along a line.


NONMAXSUP
-

Function for performing non
-
maxima suppression on an image using
an

orientation image.

It is assumed that the
orientation image gives

feature normal orientation
angles in degrees (0
-
180).


Normalization techniques
available[3]:




Daugman’s

rubber sheet model, [5]


Image registration technique, [7]


Virtual circles technique, [8]

Normalization technique in Masek's
method [3]

Normalization is performed to eliminate dimensional
inconsistencies between iris regions. For normalization of
iris region a technique based on Daugman's rubber sheet
model [5] is implemented.

The center of the pupil is considered as the reference
point, and

radial vectors pass through the iris region.



A number of data

points are selected along each radial
line and this is defined as the radial resolution.



.




The number of radial lines going around the iris region is
defined as the angular

resolution.

A constant number of
points are chosen along each radial line, so that a
constant

number of radial data points are taken,
irrespective of how narrow or wide the radius

is at a
particular angle.



The normalized pattern is created by backtracking to find
the

Cartesian coordinates of data points from the radial
and angular positions in the normalized pattern.

Feature Extraction and
Encoding

techniques available [
3]



Gabor filters [3]


Log Gabor filters (used by
Masek
, [4])


Zero crossings of 1D wavelet (used by Boles
et al, [8])


Laplacian

of Gaussian filters (used by
Wildes

et al, [7])

Techniques used for matching of
pattern[3]:



Hamming distance (employed by
Daugman
)

[3],
[5]


Weighted Euclidean distance (used by Zhu et al,
[18])


Normalized correlation (used by
Wildes

et al,
[7])


Functions involved in steps 2 and 3:
-

Normalization and Encoding [3], [10], [11]


normaliseiris
-

normalization of the iris region
by

unwrapping the circular region into a rectangular
block of




constant dimensions.


encode
-

generates a biometric template from the
normalized iris region,

also generates corresponding
noise mask


gaborconvolve
-

function for convolving each row of
an image with 1D log
-
Gabor filters.



Matching algorithms used in L.Masek's
method [3]


For matching, the Hamming distance is chosen, since

bit
-
wise comparison is required. The Hamming distance
algorithm employed

also incorporates noise masking, so
that only significant bits are used in calculating

the
Hamming distance between two iris templates.



When taking the Hamming

distance, only those bits in the
iris pattern that correspond to ‘0’ bits in noise masks of

both iris patterns is used in the calculation involved in the
matching of pattern [3].



Functions involved in step 4 :
-

Matching

[3], [10], [11]


gethammingdistance
-

returns the Hamming Distance
between two iris templates

incorporates noise masks, so
noise bits are not used for

calculating the HD.


shiftbits
-

function to shift the bit
-
wise iris patterns in
order to provide the best match. Each shift is by two bit
values and left to right, since one pixel value in
the

normalized iris pattern gives two bit values in the
template
.


Test Results



1. Output Segmented Images

Figure 2:

001_1_1.bmp

Figure 1:
001_1_3.bmp


Figure 3:
Img_2_1_1.jpg

Figure 4:
Img_2_1_2.jpg

2. Output Normalized Images

Figure 5:
001_1_1.bmp

Figure 6:
001_1_3.bmp


Figure 7:
Img_2_1_1.jpg

Figure8:
Img_2_1_2.jpg

3. Output Noise Images


Figure 9:
001_1_1.bmp

Figure10:
001_1_3.bmp


Figure 11:
Img_2_1_1.jpg

Figure 12:
Img_2_1_2.jpg

4. Output Polar Noise Images


Figure 13:
001_1_1.bmp

Figure 14:
001_1_3.bmp


Figure 15:
Img_2_1_1.jpg

Figure 16:
Img_2_1_2.jpg

Table 1 shows the calculated Hamming distance
for the four tests conducted.


If the Hamming distance calculated is less than a
preset Hamming distance (It is 0.4 for the tests
conducted, [3]), the images are said to be related;
else the images are different.

Test
Number

Input 1

Input 2

Hamming
Distance

Match
Found/No
Match
Found

1.

001_1_1.

bmp

001_1_3.

bmp

0.2647

Match
Found

2.

Img_2_1_1
.jpg

Img_2_1_2
.jpg

0.1506

Match
Found

3.

001_1_1.

bmp

001_1_1.

bmp

0

Match
Found

4.

001_1_1.

bmp


Img_2_1_1
.jpg


0.4454

No Match
Found

Table 1 : Calculated Hamming distance for four pairs of test
inputs

REFERENCES


[1] J. Daugman, "High confidence visual recognition of persons by
a test of statistical independence",
IEEE Transactions on Pattern
Analysis and Machine

Intelligence
, Vol.15, No.11, pp.1148
-
1160,
November, 1993.


[2]

J. Daugman, " How iris recognition works", IEEE Transactions
on Circuits and Systems for Video Technology, Vol.14, No.1, pp.21
-
30, January, 2004.


[3] L. Masek, "Recognition of human iris patterns for biometric
identification", M.S. thesis, University of Western Australia, 2003.


[4]

R. Wildes, " Iris recognition: an emerging biometric
technology",
Proceedings of

the IEEE
, Vol. 85, No. 9, pp.1348
-
1363,
September, 1997.


[5]

J. Daugman, Biometric personal identification system based on
iris analysis. United States Patent, Patent Number: 5,291,560,1994.




[6]


S. Sanderson and J. Erbetta, " Authentication for secure
environments based on iris scanning technology",
IEE Colloquium on
Visual Biometrics
, pp.8/1
-
8/7,

March, 2000.


[7]


R. Wildes, J. Asmuth, G. Green, S. Hsu, R. Kolczynski, J.
Matey and S. McBride, " A system for automated iris recognition",
Proceedings IEEE Workshop on

Applications of Computer Vision
,
Sarasota, FL, pp.121
-
128, December, 1994.


[8]

W. Boles and B. Boashash, " A human identification technique
using images of the iris and wavelet transform",
IEEE Transactions
on Signal Processing
, Vol. 46, No. 4, pp.185
-
188, April, 1998.


[9] A.

Gongazaga and R.M. da Costa, " Extraction and selection of
dynamic features of human iris", IEEE Computer Graphics and
Image Processing, Vol. XXII, pp.202
-
208, October, 2009.




[10]

P. Kovesi "
MATLAB functions for computer vision and image

analysis
", available
at:

http://www.cs.uwa.edu.au/~pk/Research/MatlabFns/index.html.


[11] L. Masek and P. Kovesi, “MATLAB source code for a
biometric identification system based on iris patterns’’, The school of
computer science and software engineering, The university of
Western Australia, 2003.


[12] D.M. Monro, S.Rakshit and Z. Dexin, "DCT based iris
recognition”, IEEE Transactions on Pattern Analysis and Machine
Intelligence, Vol. 29, Issue 4, pp.586
-
595,

April, 2007.


[13] Different sample source codes for the functions involved in
Masek's algorithm are available at:

Advancedsourcode.com:
http://www.advancedsourcecode.com/iris.asp




[14] Chinese academy of sciences
-

institute of automation,
database of greyscale eye images

http://www/cbsr.ia.ac.cn/IrisDatabase.htm



[
15] K. Miyazawa, K. Ito, K. Aoki, T. Kobayashi and K. Nakajima, "
An efficient iris recognition algorithm using phase based image
matching ", IEEE International conference on image processing,
pp.325
-
328, September, 1995.



[16] W. Kong and D. Zhang," Accurate iris segmentation based on
novel reflection and

eyelash detection model", Proceedings of 2001
International Symposium on

Intelligent Multimedia, Video and
Speech Processing, Hong Kong, pp.263
-
266, May, 2001.




[17] N. Ritter, "Location of the pupil
-
iris border in slit
-
lamp images
of the cornea", Proceedings of the International Conference on
Image Analysis and

Processing, pp.740
-
745, September, 1999.



[18] Y. Zhu, T. Tan and Y. Wang,” Biometric personal identification
based on iris

patterns” ,Proceedings of the 15th International
Conference on Pattern

Recognition, Spain,

Vol. 2, pp.801
-
804,
February, 2000.



[19] Online free encyclopedia, Wikipedia:
http://www.wikipedia.org
/
.


[20] K.R.Rao and P.Yip, ”Discrete cosine transform”, Boca Raton,
FL:

Academic press, 1990.

THANKYOU