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.
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