Human Recognition through
Iris Image Processing
Mr. Swanirbhar Majumder
Department of Electronics & Comm. Engg
North Eastern Regional Institute of Science &
Technology (NERIST)
(Deemed University)
Mr. Saurabh Pal
Department of Electronic Instrumentation Engg
Haldia Institute of Technology (HIT)
Under WBUT (West Bengal University of Technology)
Introduction
Iris
recognition
is
one
of
the
most
secure
biometric
technologies
.
Although
facial
recognition,
voice
recognition,
and
hand
geometry
are
nonintrusive
and
have
value
for
certain
applications,
all
of
these
biometric
methods
are
relatively
unreliable
compared
with
iris
recognition
Iris Facts:
The
iris
is
the
colored
part
of
the
eye
;
it
lies
behind
the
cornea
and
in
front
of
the
lens
and
is
protected
by
the
eyelid
.
The
iris
is
formed
of
a
trabecular
meshwork
(elastic
connective
tissue),
layers
of
pigment,
muscle,
and
ligaments,
and
it
controls
the
amount
of
light
that
enters
the
eye
by
allowing
the
pupil
to
dilate
.
Color
is
not
used
in
iris
-
recognition
technology
.
Instead,
the
other
visible
features
—
the
connective
tissue,
cilia,
contraction
furrows,
crypts,
rings,
and
corona
distinguish
one
iris
from
another
.
Iris Facts: (Continued)
By
the
time
a
person
is
about
eight
months
old,
the
structures
of
the
iris
are
complete,
and
they
do
not
change
in
later
life
.
The
iris
cannot
be
surgically
altered
without
damage
to
a
person’s
vision
The
purpose
of
iris
recognition
products
is
to
provide
real
-
time,
high
-
confidence
recognition
of
a
person’s
identity
Iris Facts: (Continued)
The
statistical
probability
that
two
irises
would
ever
be
exactly
the
same
is
estimated
at
1
in
10
72
.
Iris
recognition
is
statistically
more
accurate
than
DNA
testing
.
No
two
irises
are
alike,
not
even
among
twins
.
In
fact,
left
and
right
irises
of
one
individual
are
not
identical
Irises
(pigmented,
round,
contractile
membranes
in
the
eyes)
are
unique
in
each
individual
Overview:
The
Iris
as
a
Biometrics
:
The
iris
is
an
overt
body
that
is
available
for
remote
assessment
with
the
aid
of
a
machine
vision
system
to
do
automated
iris
recognition
.
Iris
recognition
technology
combines
computer
vision,
pattern
recognition,
statistical
inference,
and
optics
.
The
spatial
patterns
that
are
apparent
in
the
human
iris
are
highly
distinctive
to
an
individual
.
Advantages:
Its suitability as an exceptionally accurate biometric derives from its:
extremely data
-
rich physical structure
genetic independence
—
no two eyes are the same
patterns apparently stable throughout life
physical protection by a transparent window (the cornea), highly
protected by internal organ of the eye
externally visible, so noninvasive
—
patterns imaged from a
distance
Disadvantages:
The disadvantages to use iris as a biometric measurement are:
Small target (1 cm) to acquire from a distance (about 1 m)
Moving target
Located behind a curved, wet, reflecting surface
Obscured by eyelashes, lenses, reflections
Partially occluded by eyelids, often drooping
Deforms non
-
elastically as pupil changes size
RECOGNITION PROCESS:
Steps:
1.
Image
capture
:
Iris
recognition
begins
with
a
video
picture
of
the
eye
and
iris
within
it
.
A
monochrome
CCD
is
the
type
of
camera
used
.
2.
Image
acquisition
:
Since
iris
is
small
in
size
and
dark
in
color,
it
is
difficult
to
acquire
good
images
for
analysis
using
the
standard
CCD
camera
and
ordinary
lighting
.
Image
acquisition
provides
iris
image
of
sufficiently
high
quality
.
We
have
so
far
got
the
eye
image
after
image
acquisition
from
the
camera
.
Steps: (Continued)
3.
Preprocessing
:
It
is
done
to
remove
some
irrelevant
parts
(e
.
g
.
eyelid,
pupil
etc
.
)
from
the
image
.
Detected
the
iris
after
preprocessing
.
Applying
the
MBIR
[Minimum
Bounding
Isothetic
Rectangle
(Square)]
technique
to
get
the
iris
as
for
normalization
by
removing
the
unnecessary
parts
.
Here
we
apply
histogram
equalization
and
get
the
resultant
MBIR
image
Steps: (Continued)
4.
Database
creation
:
A
database
of
the
normalized
iris
is
created
so
that
whenever
an
iris
image
is
got
the
normalized
pattern
of
that
iris
can
be
checked
to
recognize
the
concerned
person
.
Here
we
use
canny’s
edge
detection
technique
to
get
the
iris
pattern
from
the
MBIR
-
ed
image
.
For
each
person
though
we
had
20
images
of
each
eye,
but
here
presently
we
used
5
edge
detected
MBIR
images
and
compare
the
intra
class
correlation
coefficient
.
The
lowest
of
the
correlation
coefficients
was
taken
with
5
percent
tolerance
to
be
the
optimum
correlation
coefficient
.
Then
we
store
in
memory
the
optimum
correlation
coefficient
along
with
any
one
of
the
edge
detected
binary
MBIR
images
.
.
This
we
carry
on
for
25
persons
.
All
these
are
done
in
.
mat
format
of
MATLAB®
.
Thus
we
develop
a
database
of
25
concerned
persons
.
Steps: (Continued)
5.
Matching
:
The
matching
process
includes
providing
of
an
eye
image
as
input,
from
which
we
get
the
last
Fig
as
output
after
all
computations
and
detections
.
Then
the
output
image’s
logical
or
binary
format
is
correlated
with
all
the
25
databases
and
the
correlation
coefficient
value
using
equation
is
compared
with
the
optimum
value
stored
in
the
database
.
If
the
correlation
coefficient
is
greater
than
the
value
in
the
database
than
we
take
it
for
MATCHED
with
that
specific
person
and
recognize
him/she
else
if
the
input
image
does
not
provide
optimum
correlation
then
it
is
taken
for
granted
that
the
input
image
is
of
an
unrecognized
person
.
RESULTS:
Created
the
database
of
25
images
with
their
respective
optimum
correlation
coefficients
Then
checked
these
for
10
images
each
for
25
persons
and
other
25
persons
whose
normalized
images
were
not
present
in
the
database
whose
correlation
coefficients
are
tabulated
as
in
Table
I
.
Then
we
used
250
(
25
X
10
)
for
match
and
250
(
25
X
10
)
for
non
-
match
thus
total
of
500
input
images
provided
we
got
227
matches
and
273
non
-
matches
of
which
we
got
only
two
false
matches
and
25
false
non
-
matches
.
The
details
are
as
tabulated
in
Table
II
.
RESULTS: (Continued)
The
histogram
plot
of
inter
and
intra
matching
of
initial
125
images
(
25
X
5
)
are
as
in
Fig
.
From
which
we
can
see
that
the
out
of
15500
data
CONCLUSION:
We
have
achieved
success
till
this
point
with
around
5
%
error,
the
main
task
is
still
left
as
our
technique
is
offline
and
non
adaptive
.
We
still
have
the
problem
of
normalizing
images
that
are
taken
with
the
person
wearing
glasses
or
contact
lens
or
for
images
where
the
eyelashes
cover
the
iris
region
.
We
are
planning
to
add
two
dimensional
Gabor
wavelets
to
filter
the
images
to
get
better
correlation
coefficients
to
avoid
further
false
matches
or
false
non
match
and
decrease
the
error
percentage
.
We
can
say
that
as
we
are
storing
the
images
in
logical
or
binary
form
the
representation
of
each
pixel
is
either
0
or
1
so
we
can
store
8
pixels
in
a
byte
whereas
if
we
had
kept
the
traditional
format
of
unsigned
integers
of
8
bits
e
would
require
a
byte
for
each
pixel
.
ACKNOLEDGEMENT:
We
would
mainly
like
to
thank
Mr
.
Soumyadip
Rakshit,
Researcher
in
Biometric
Signal
Processing
of
Signal
and
Image
Processing
Group
(SIPG),
Department
of
Electronic
and
Electrical
Engineering
at
the
University
of
Bath
who
provided
us
the
database
created
by
them
for
iris
image
processing
.
The
database
consisted
of
20
images
of
both
eyes
of
50
persons
amounting
to
2000
images
.
Along
with
that
we
would
also
like
to
thank
Prof
.
B
.
N
.
Chatterji
who
actually
motivated
us
to
work
in
this
field
and
in
this
context
using
MBIR
technique
.
REFERENCES:
[
1
]
S
.
Sanderson,
J
.
Erbetta
.
Authentication
for
secure
environments
based
on
iris
scanning
technology
.
IEE
Colloquium
on
Visual
Biometrics
,
2000
.
[
2
]
J
.
Daugman
.
How
iris
recognition
works
.
Proceedings
of
2002
International
Conference
on
Image
Processing,
Vol
.
1
,
2002
.
[
3
]
J
.
Daugman
.
Biometric
personal
identification
system
based
on
iris
analysis
.
United
States
Patent,
Patent
Number
:
5
,
291
,
560
,
1994
.
[
4
]
R
.
Wildes
.
Iris
recognition
:
an
emerging
biometric
technology
.
Proceedings
of
the
IEEE
,
Vol
.
85
,
No
.
9
,
1997
.
[
5
]
R
.
Wildes,
J
.
Asmuth,
G
.
Green,
S
.
Hsu,
R
.
Kolczynski,
J
.
Matey,
S
.
McBride
.
A
system
for
automated
iris
recognition
.
Proceedings
IEEE
Workshop
on
Applications
of
Computer
Vision
,
Sarasota,
FL,
pp
.
121
-
128
,
1994
.
REFERENCES: (Continued)
[
6
]
W
.
Boles,
B
.
Boashash
.
A
human
identification
technique
using
images
of
the
iris
and
wavelet
transform
.
IEEE
Transactions
on
Signal
Processing
,
Vol
.
46
,
No
.
4
,
1998
.
[
7
]
S
.
Lim,
K
.
Lee,
O
.
Byeon,
T
.
Kim
.
Efficient
iris
recognition
through
improvement
of
feature
vector
and
classifier
.
ETRI
Journal
,
Vol
.
23
,
No
.
2
,
Korea,
2001
.
[
8
]
S
.
Noh,
K
.
Pae,
C
.
Lee,
J
.
Kim
.
Multiresolution
independent
component
analysis
for
iris
identification
.
The
2002
International
Technical
Conference
on
Circuits/Systems,
Computers
and
Communications
,
Phuket,
Thailand,
2002
.
[
9
]
Libor
Masek,
School
of
Computer
Science
and
Software
Engineering,
The
University
of
Western
Australia,
2003
[
10
]
S
.
Rakshit
and
others
of
Signal
and
Image
Processing
Group
(SIPG),
Department
of
Electronic
and
Electrical
Engineering
at
the
University
of
Bath
,
http
:
//www
.
bath
.
ac
.
uk
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