STUDY OF IRIS RECOGNITION SCHEMES

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STUDY OF IRIS RECOGNITION SCHEMES







Under guidance of

DR K R RAO

UNIVERSITY OF TEXAS AT ARLINGTON

SPRING 2012






Presented by:

Ritika Jain

ritika.jain@mavs.uta.edu

1000797700




Proposal:

This project focu
s
ses upon studying

and implementing

the various iris
recognition schemes availab
l
e and an analysis of the different algorithms using
Chinese academy of sciences
-
institute of automation (
CASIA
)
[14]

database.

Overview

about iris[19]
:

Iris recognition is amongst the most robust and accurate b
iometric technique
available in the market today with existing large scale applications supporting
databases in excess of millions of people.
The

iris

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
. Eye color is the color of the iris, which
can be green, blue, or brown. In some cases it can be hazel (a combination of
light brown, green and gold), grey, violet, or even pink. In response to the
a
mount of light entering the eye, muscles attached to the iris expand or contract
the aperture at the center of the iris, known as the

pupil
. The larger the pupil,
the more light can enter.

The iris is a protected organ whose random texture is stable throug
hout the life
and hence can be used as an identity document offering a very high degree of
identity assurance.



Figur
e 1 shows the location of iris in the human eye.



Figure1:Location of iris in human eye

[19]



Advantages of using iris as a recognition

scheme[19]:



Iris is an internal organ that is well protected against damage and wear by a
highly transparent and sensitive membrane (the cornea). This distinguishes it
from fingerprints, which can be difficult to recognize after years of certain types
of
manual labor.



The iris is mostly flat, and its geometric configuration is only controlled by two
complementary muscles (the sphincter pupillae and dilator pupillae) that control
the diameter of the pupil. This makes the iris shape far more predictable tha
n,
for instance, that of the face.



An iris scan is similar to taking a photograph and can be performed from about
10

cm to a few meters away.



There is no need for the person being identified to touch any equipment that has
recently been touched by a stran
ger, thereby eliminating an objection that has
been raised in some cultures against fingerprint scanners, where a finger has to
touch a surface, or retinal scanning, where the eye must be brought very close
to an eyepiece (like looking into a

microscope) .


Disadvantages of using iris for identification

[19]
:



As with other photographic biometric technologies, iris recognition is
susceptible to poor image quality, with assoc
iated failure to enroll rates for
recognition with respect to matching of images.



Many commercial Iris scanners can be easily fooled by a high quality image of
an iris or face in place of the real thing.





The accuracy of scanners can be affected by changes in
lighting.



As with other photographic biometric technologies, iris recognition

is
susceptible to poor image quality, with asso
ciated failure to enroll rates for

recognition with respect to mat
ching of images.


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 templ
ates in order to determine identity.

Most biometric systems allow two modes of operation namely enrolment and
identification.

In the proposed project also these two modes are used.


Overview of the proposed project:



In the project the various algorithms
are discussed and a
nalyz
ed like as
proposed by

Daugman

[2],

[5],

[6];
Recognition of human i
ris patterns for
biometric ide
ntification as proposed by

Masek

[10],

[11];


Phase based iris
identification by Miyawaza

[15],

d
iscrete cosine transform

(DCT)


[20]

based
Iris recognition by Monro et al

[16]

and other like techniques available.




The various functions, filters and the processes involved will be studied and the
r
esults are

compared on the basis of previous studies done using the same
method.



CASIA iris

image database

[14] will be used

for images of iris to be analyz
ed
for the different codes
.



By understanding the techniques available and doing a comparative study an
analysis
will be done

about the features involved,

the a
dvantages and the
shortcomings a
nd the project will be extended for a more genuine detection.


Future scope and extension:

The project can be extended to include a
vast

database with increased genuine
detection which involves forming more templates and improvising the current
code. The h
ardware of the equipment
can be worked upon
and improvised
,

which is used to capture the image
s

to improve the performance
.


References:




[1
]
J. Daugman, "High confidence visual recognition of persons by a test of
statistical independence",

IEEE
Transactions on Pattern Analysis and
M
achine

I
ntelligence
, Vol. 15, No. 11,

pp.
1148
-
1160, November,

1993.



[2]
J. Daugman, " How iris recognition works",


IEEE Trans
action
s

o
n

circuit
s and

system
s
for
vi
deo 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. Kolc
zynski, 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.1185
-
1188,

April,

1998.




[9]A.

Gongazaga and R.
M. da C
osta,

"
Extraction and selection of dynamic
features

of human iris", IEEE Computer Graphics and Image P
rocessin
g
,

Vol.
XXII,

pp.
202
-
208

, October,

2009.



[10]

P. Kove
si
,"
MATLAB functions for computer vision and i
mage

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 s
yst
em based on iris
patterns’’, The

school of computer
science and software engineering, The university of w
estern Australia,

2003
.




[12]D.M. Monro,

S.Rakshit and Z. Dexin,

"DCT based iris recognition",

IEEE

Transactions on

p
attern analysis and machine inte
lligence, Vol.

29,

Issue 4, pp.
58
6
-
595,

April,

2007.




[13]Different
sample source codes available at:

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




[14] Chinese Academy of Sciences


Instit
ute of Automation. Database of
greyscale eye i
mages

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

Internat
ional
Co
nference on Image processing, pp.
325
-
328,

September,

1995.







[16] W. Kong and D. Zhang,

"
Accurate iris segmentation based on novel
reflectio
n 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 Im
age Analysis
and

Processing, pp.
740
-
745, September,

1999.





[18] Y. Zhu, T. Tan and Y.
Wang,

''

Biometric personal identi
fication based
on iris

patterns'',

Proceed
ings of the 15th International Conference on
P
attern

R
ecognition, 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.