Iris Scan Presentation - 123SeminarsOnly

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30 Νοε 2013 (πριν από 3 χρόνια και 7 μήνες)

127 εμφανίσεις

By

Roll
No:
10224002

M.Tech

(Computer
Tech.)

NIT


Under the guidance of

Assistant
professor

Dept. of Electrical
Engineering

NIT

P
ERSONAL

I
DENTIFICATION

B
ASED

O
N

I
RIS

P
ATTERN

CONTENTS

1.INTRODUCTION OF IRIS RECOGNITION



What is Iris Recognition



Human Iris



Operating Principle



Advantages



Disadvantages



History

2
.

STATE OF
THE ART




C
ONTENTS

(C
NTD
….)

3. TECHNICAL ISSUES


Image
Acquisition



Segmentation



Normalization



Feature Encoding And Matching



Iris Image Database

4
PERFORMANCE METRICS FOR IRIS RECOGNITION

5. APPLICATIONS OF IRIS RECOGNITION

6. REFERENCES

1. INTRODUCTION OF IRIS
RECOGNITION

1.1 What Is Iris Recognition




Iris
recognition

is a method of

biometric

authentication
that uses pattern
-
recognition techniques based on high
-
resolution images of
the iris

of an individual's

eyes.


A Iris recognition system provides Personal identification
of an individual based on a unique feature or characteristic
possessed by the human Iris
.


The physiological complexity of the organ results in the
random
patterns
in iris, which are statistically unique and
suitable for biometric
measurements.


INTRODUCTION OF IRIS RECOGNITION

(C
NTD
….)

1.2 Human Iris


The iris is a thin circular diaphragm, which lies between the
cornea and the lens of the human eye
.
front
-
on view of the iris is
shown in Figure 1.1.










Figure
1.1


A front
-
on view of the human eye.




1.2 H
UMAN

I
RIS

(
CNTD
….)


The

iris

is

perforated

close

to

its

centre

by

a

circular

aperture

known

as

the

pupil
.



The

function

of

the

iris

is

to

control

the

amount

of

light

entering

through

the

pupil,

and

this

is

done

by

the

sphincter

and

the

dilator

muscles,

which

adjust

the

size

of

the

pupil
.

The

average

diameter

of

the

iris

is

12

mm,

and

the

pupil

size

can

vary

from

10
%

to

80
%

of

the

iris

diameter

.


The

iris

consists

of

a

number

of

layers,

the

lowest

is

the

epithelium

layer,

which

contains

dense

pigmentation

cells
.

The

stromal

layer

lies

above

the

epithelium

layer,

and

contains

blood

vessels,

pigment

cells

and

the

two

iris

muscles
.

The

density

of

stromal

pigmentation

determines

the

colour

of

the

iris
.



1.2 H
UMAN

I
RIS

(
CNTD
….)


Formation

of

the

iris

begins

during

the

third

month

of

embryonic

life

[
3
]
.

The

unique

pattern

on

the

surface

of

the

iris

is

formed

during

the

first

year

of

life,

and

pigmentation

of

the

stroma

takes

place

for

the

first

few

years
.

Formation

of

the

unique

patterns

of

the

iris

is

random

and

not

related

to

any

genetic

factors

[
4
]
.


The

only

characteristic

that

is

dependent

on

genetics

is

the

pigmentation

of

the

iris,

which

determines

its

colour
.

Due

to

the

epigenetic

nature

of

iris

patterns,

the

two

eyes

of

an

individual

contain

completely

independent

iris

patterns,

and

identical

twins

possess

uncorrelated

iris

patterns[
3
]
.



INTRODUCTION OF IRIS RECOGNITION

(C
NTD
….)

1.3 Operating Principle


An

iris
-
recognition

algorithm

first

has

to

identify

the

approximately

concentric

circular

outer

boundaries

of

the

iris

and

the

pupil

in

a

photo

of

an

eye
.


The

set

of

pixels

covering

only

the

iris

is

then

transformed

into

a

bit

pattern

that

preserves

the

information

that

is

essential

for

a

statistically

meaningful

comparison

between

two

iris

images
.


To

authenticate

via

identification

or

verification,

a

template

created

by

imaging

the

iris

is

compared

to

a

stored

value

template

in

a

database
.


If

the

Hamming

Distance

is

below

the

decision

threshold,

a

positive

identification

has

effectively

been

made(HD<=
0
.
32
)
.


1.3
O
PERATING

P
RINCIPLE

(C
NTD
….)


A

practical

problem

of

iris

recognition

is

that

the

iris

is

usually

partially

covered

by

eyelids

and

eyelashes
.

In

order

to

reduce

the

false
-
reject

risk

in

such

cases,

additional

algorithms

are

needed

to

identify

the

locations

of

eyelids

and

eyelashes

and

to

exclude

the

bits

in

the

resulting

code

from

the

comparison

operation


Human iris identification process is basically divided into four steps
,


i.
Localization

-

The

inner

and

the

outer

boundaries

of

the

iris

are

calculated
.


ii.
Normalization

-

Iris

of

different

people

may

be

captured

in

different

size,

for

the

same

person

also

size

may

vary

because

of

the

variation

in

illumination

and

other

factors
.


iii.
Feature

extraction

-

Iris

provides

abundant

texture

information
.

A

feature

vector

is

formed

which

consists

of

the

ordered

sequence

of

features

extracted

from

the

Various

representations

of

the

iris

images
.


iv.
Matching

-

The

feature

vectors

are

classified

through

different

thresholding

techniques

like

Hamming

Distance,

weight

vector

and

winner

selection,

dissimilarity

function,

etc
.

1.3
O
PERATING

P
RINCIPLE

(C
NTD
….)






Image for explaining Identification process


I
RIS

RECOGNITION

SYSTEM



INTRODUCTION OF IRIS RECOGNITION

(C
NTD
….)

1.4 Advantages


The

iris

of

the

eye

has

been

described

as

the

ideal

part

of

the

human

body

for

biometric

identification

for

several

reasons


It 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 than, for instance, that of the face.


1.4 A
DVANTAGES

(C
NTD
)


The

iris

has

a

fine

texture

that

like

fingerprints

is

determined

randomly

during

embryonic

gestation

.

Like

the

fingerprint,

it

is

very

hard

(if

not

impossible)

to

prove

that

the

iris

is

unique
.

However,

there

are

so

many

factors

that

go

into

the

formation

of

these

textures

(the

iris

and

fingerprint)

that

the

chance

of

false

matches

for

either

is

extremely

low
.

Even

genetically

identical

individuals

have

completely

independent

iris

textures
.


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

to

be

identified

to

touch

any

equipment

that

has

recently

been

touched

by

a

stranger,

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

can

be

brought

very

close

to

a

lens

(like

looking

into

a

microscope

lens)
.
The

originally

commercially

deployed

iris
-
recognition

algorithm,

John

Daugman's

Iris

Code,

has

an

unprecedented

false

match

rate


1.4 A
DVANTAGES

(C
NTD
)


While there are some medical and surgical procedures that
can affect the color and overall shape of the iris, the fine
texture remains remarkably stable over many decades.

Some Iris identificationn have succeeded over a period
about 30 year.


INTRODUCTION OF IRIS RECOGNITION

(C
NTD
….)

1.5 Disadvantage


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

scanners

are

often

tough

to

adjust

and

can

become

bothersome

for

multiple

people

of

different

heights

to

use

in

succession
.


No

one

is

completely

sure

how

an

infrared

light

could

potentially

damage

eyesight

and

many

feel

that

it

should

have

been

heavily

researched

before

it

was

marketed

and

sold
.

The

accuracy

of

scanners

can

be

affected

by

changes

in

lighting
.


Iris

recognition

is

very

difficult

to

perform

at

a

distance

larger

than

a

few

meters

and

if

the

person

to

be

identified

is

not

cooperating

by

holding

the

head

still

and

looking

into

the

camera
.

However,

several

academic

institutions

and

biometric

vendors

are

developing

products

that

claim

to

be

able

to

identify

subjects

at

distances

of

up

to

10

meters


As

with

other

photographic

biometric

technologies,

iris

recognition

is

susceptible

to

poor

image

quality,

with

associated

failure

to

enroll

rates
.


INTRODUCTION OF IRIS RECOGNITION

(C
NTD
….)

1.5 History


The

history

of

iris

recognition

goes

back

to

mid

19
th
-
century

when

the

French

physician,

Alphonse

Bertillon,

studied

the

use

of

eye

color

as

an

identifier

[
2
]
.



However
,

it

is

believed

that

the

main

idea

of

using

iris

patterns

for

identification
,

the

way

we

know

it

today,

was

first

introduced

by

an

eye

surgeon,

Frank

Burch,

in

1936

[
6
]
.



In

1987
,

two

ophthalmologists,

Flom

and

Safir,

patented

this

idea

and

proposed

it

to

Daugman,

a

professor

at

Harvard

University
,

to

study

the

possibility

of

developing

an

iris

recognition

algorithm
.



After

a

few

years

of

scientific

experiments,

Daugman

proposed

and

developed

a

high

condense

iris

recognition

system

and

published

the

results

in

1993
.

The

proposed

system

then

evolved

and

achieved

better

performance

in

time

by

testing

and

optimizing

it

with

respect

to

large

iris

databases
.

1.5 H
ISTORY

(C
NTD
…..)


A

few

years

after

the

publication

of

the

First

algorithm

by

Daugman,

other

researchers

developed

new

iris

recognition

algorithms
.



Systems

presented

by

Wildes

et

al
.

[
11
],

Boles

and

Boashash

,

Tisse

et

al
.
,

Zhu

et

al
.
,

Lim

et

al
.
,

Noh

et

al
.


and

Ma

et

al
.

are

some

of

the

well
-
known

algorithms

so

far
.



Among

these

algorithms,

the

works

done

by

Lim

et

al
.

and

Noh

et

al
.

are

also

commercialized
.



The

algorithms

developed

by

Wildes

and

Boles

are

suitable

for

verification

applications

because

the

normalization

of

irises

is

performed

in

the

matching

process

and

would

be

very

time

consuming

in

identification

applications
.



Although

these

algorithms

have

been

successful,

they

still

require

to

be

improved

in

the

accuracy

and

speed

aspects

compared

to

the

proposed

algorithm

by

Daugman
.


2. S
TATE

OF

THE

ART



For

instance,

the

developed

algorithm

by

Daugman
,

which

is

known

as

the

state
-
of
-
the
-
art

in

the

field

of

iris

recognition,

has

initiated

huge

investments

on

the

technology

for

more

than

a

decade
.

IriScan

Inc
.

patents

the

core

technology

of

the

Daugman's

system

and

several

companies

such

as

IBM,

Iridian

Technologies,

IrisGuard

Inc
.
,

Securimetrics

Inc
.

and

Panasonic

are

active

in

providing

iris

recognition

products

and

services
.


Even

though

the

Daugman

system

is

the

most

successful

and

most

well

known,

many

other

systems

have

been

developed
.

The

most

notable

include

the

systems

of

Wildes

et

al
.
,

Boles

and

Boashash,

Lim

et

al
.
,

and

Noh

et

al
.



The

algorithms

by

Lim

et

al
.

are

used

in

the

iris

recognition

system

developed

by

the

Evermedia

and

Senex

companies
.

Also,

the

Noh

et

al
.

algorithm

is

used

in

the

‘IRIS
2000


system,

sold

by

IriTech
.

These

are,

apart

from

the

Daugman

system,

the

only

other

known

commercial

implementations
.




2. S
TATE

OF

THE

ART


(C
NTD
…)


The Daugman system has been tested under numerous
studies, all reporting a zero failure rate. The Daugman system
is claimed to be able to perfectly identify an individual, given
millions of possibilities. The prototype system by Wildes et al.
also reports flawless performance with 520 iris images
,
and
the Lim et al. system attains a recognition rate of 98.4% with
a database of around 6,000 eye images
.


Compared with other biometric technologies, such as face,
speech and finger recognition, iris recognition can easily be
considered as the most reliable form of
biometric technology .


However
, there have been no independent trials of the
technology, and source code for systems is not available. Also,
there is a lack of publicly available datasets for testing and
research, and the test results published have usually been
produced using carefully imaged irises under favourable
conditions.



3. TECHNICAL ISSUES


3.1 IMAGE
ACQUISITION


Why important?


One of the major challenges of automated iris
recognition is to capture a high
-
quality image of the
iris while remaining noninvasive to the human
operator.


Concerns on the image acquisition rigs


Obtained images with sufficient resolution and
sharpness


Good contrast in the interior iris pattern with proper
illumination


Well centered without unduly constraining the operator


Artifacts eliminated as much as possible


3.1 IMAGE
ACQUISITION

(C
NTD
…..)


Image Acquisition


Rigs

a.The

Daugman image
-
acquisition rig




b.

The Wildes
et al.
image
-
acquisition rig


I
MAGE

A
CQUISITION

(C
NTD
……)


Image Acquisition


Results









Result Image from Wildes et al. rig
--

capture the iris as part of
a larger image that also contains data derived from the
immediately surrounding eye region

3.1 I
MAGE

A
CQUISITION

(C
NTD
……)


Discussion

In common:


Easy for a human operator to master


Use video rate capture


Difference:
.


Operator self
-
position


The Daugman’s system provides the operator with live video
feedback


The Wildes
et al.
system provides a reticle to aid the operator in
positioning


3. TECHNICAL ISSUES

(C
NTD
….)

3.2 SEGMENTATION :




In
segmentation, it is desired to distinguish the iris texture
from the rest of the image.
An iris
is normally segmented
by detecting its inner (pupil) and outer (
limbus)
boundaries.



Well
-
known
methods such as the
Integro
-
differential
,
Hough transform and active
contour models
have been
successful techniques in detecting the boundaries
.
In the
following, these methods are described and some of their
weaknesses are pointed out.


Iris Segmentation algorithm performed following steps

i.
Reflection Removal and Iris Detection

ii.
Pupillary

and Limbic Boundary Localization(Iris
Localization)

iii.
Eyelid Localization

iv.
Eyelashes and shadow detection


3.2 SEGMENTATION

(C
NTD
)


Segmentation
Alogorithm


3.2 SEGMENTATION

(C
NTD
)

3.2.1
Daugman's
Integro
-
differential Operator


In

order

to

localize

an

iris,

Daugman

proposed

the

Integro
-
differential

operator
.

The

operator

assumes

that

pupil

and

limbus

are

circular

contour

and

performs

as

a

circular

Edge

detector

.

Detecting

the

upper

and

lower

eyelids

are

also

performed

using

the

Integro
-
differential

operator

by

adjusting

the

contour

search

from

circular

to

a

designed

arcuate
.

The

Integro
-
differential

is

defned

as









The

operator

pixel
-
wise

searches

throughout

the

raw

input

image,

I
(
x,y
),

and

obtains

the

blurred

partial

derivative

of

the

integral

over

normalized

circular

contours

in

different

radii
.






3.2.1
D
AUGMAN
'
S

I
NTEGRO
-
DIFFERENTIAL

O
PERATOR

(C
NTD
…)





The
pupil and limbus
boundaries
are
expected
to maximize
the contour
integral
derivative, where the intensity values
over the circular b orders would make a sudden change.
G
σ
(r)

is a smoothing function controlled by
σ

that
smoothes
the image intensity for a more precise search.


3.2 SEGMENTATION

(C
NTD
)

3.2.2 Hough Transform :


First, the image intensity information is converted
into a
binary edge
-
map




Where




And





Second, the edge points vote to instantiate particular contour
parameter values







3.2.2 H
OUGH

T
RANSFORM

:

(C
NTD
….)


The voting procedure of the Wildes
et al.
system is realized
via
Hough transforms

on parametric definitions of the iris
boundary contours.



3. TECHNICAL ISSUES

(C
NTD
….)

3.3 NORMALIZATION :


Normalization refers to preparing a segmented iris image for the
feature extraction pro
cess
. In Cartesian co ordinates, iris images
are highly
a®ected

by their distance and angular position with
resp

ect

to the camera. Moreover, illumination has a direct impact
on pupil size and causes non
-
linear variations of the iris patterns.
A prop
er

normalization technique is exp
ected

to transform the
iris image to comp
ensate

these variations.


Methematical

Tools For Normalization

3.3.1 Daugman's Cartesian to Polar Transform

3.3.2 Wildes' Image Registration

3.3.3 Virtual Circles






3. TECHNICAL ISSUES

(C
NTD
….)

3.4 FEATURE ENCODING AND MATCHING


In

order

to

provide

accurate

recognition

of

individuals,

the

most

discriminating

information

present

in

an

iris

pattern

must

be

extracted
.

Only

the

significant

features

of

the

iris

must

be

encoded

so

that

comparisons

between

templates

can

be

made
.


Mathematical

Tools

For

Feature

Encoding

3
.
4
.
1

Wavelet

Encoding

3
.
4
.
2

Gabor

Filters

3
.
4
.
3

Log
-
Gabor

Filters

3
.
4
.
4

Zero
-
crossings

of

the

1
D

wavelet

3
.
4
.
5

Haar

Wavelet





3.4 FEATURE ENCODING AND
MATCHING

(C
NTD
)


The

template

that

is

generated

in

the

feature

encoding

process

will

also

need

a

corresponding

matching

metric,

which

gives

a

measure

of

similarity

between

two

iris

templates
.

This

metric

should

give

one

range

of

values

when

comparing

templates

generated

from

the

same

eye,

known

as

intra
-
class

comparisons,

and

another

range

of

values

when

comparing

templates

created

from

different

irises,

known

as

inter
-
class

comparisons
.

These

two

cases

should

give

distinct

and

separate

values,

so

that

a

decision

can

be

made

with

high

confidence

as

to

whether

two

templates

are

from

the

same

iris,

or

from

two

different

irises
.


Mathematical

Tools

For

Matching

3
.
4
.
6

Hamming

distance

3
.
4
.
7

Weighted

Euclidean

Distance





3. TECHNICAL ISSUES

(C
NTD
….)

3.5 IRIS IMAGE DATABASE


The accuracy of the iris recognition system depends on the image
quality of the iris images. Noisy and low quality images degrade


the performance of the system.


Some Iris image database available are

I.
UBIRIS

II.
CASIA

III.
LEA

IV.
MMU

V.
ICE database

4. PERFORMANCE METRICS FOR


IRIS RECOGNITION


The following are used as performance metrics
for Iris Recognition systems

i.
False accept rate or false match rate (FAR or
FMR
):
The probability that the system incorrectly matches the input
pattern to a non
-
matching template in the database. It measures the
percent of invalid inputs which are incorrectly accepted.

ii.
F
alse reject rate or false non
-
match rate (FRR or FNMR):

the
probability that the system fails to detect a match between the input
pattern and a matching template in the database. It measures the
percent of valid inputs which are incorrectly rejected.

iii.
Fqual

error rate or crossover error rate (EER or CER)
: the rate at
which both accept and reject errors are equal. The value of the EER can
be easily obtained from the ROC curve. The EER is a quick way to
compare the accuracy of devices with different ROC curves. In general,
the device with the lowest EER is most accurate.

iv.
Failure to enroll rate (FTE or FER)
:the rate at which attempts to
create a template from an input is unsuccessful. This is most commonly
caused by low quality inputs





4. PERFORMANCE METRICS FOR


IRIS RECOGNITION (C
NTD
…..)

6. Failure to capture rate (FTC)
:
Within automatic systems,
the probability that the system fails to detect a biometric input
when presented correctly.

7.

template capacity
: The maximum number of sets of data which
can be stored in the system.




5. APPLICATION OF IRIS RECOGNITION



Some Current and Future Applications of Iris
Recognition

1.
national border controls: the iris as a living passport.

2.
computer login: the iris as a living password.

3.
cell phone and other wireless
-
device
-
based authentication.

4.
secure access to bank accounts at cash machines.

5.
premises access control (home, office, laboratory, etc)

6.
driving licenses; other personal certificates

7.
forensics; birth certificates; tracing missing or wanted persons

8.
credit
-
card authentication

9.
credit
-
card authentication

10.
anti
-
terrorism (e.g. security screening at airports)

11.
secure financial transactions (electronic commerce, banking)

12.
Biometric
-
Key Cryptography" (stable keys from unstable
templates)

6. 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] E. Wolff. Anatomy of the Eye and Orbit. 7th edition. H. K. Lewis & Co. LTD,1976.


[4] R. Wildes. Iris recognition: an emerging biometric technology. Proceedings of the
IEEE, Vol. 85, No. 9, 1997.


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


[6] J. Daugman, “High Confidence Visual Recognition by a test of Statistical
Independence”, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 15,
No.11, pp.1148
-
1161,1993.


[7] R.P.Wildes, J.C.Asmuth, G.L. Green, S.C.Hsu, R.J,Kolczynski, J.R.Matey,
S.E.McBride, David Sarno_ Res. Center, Princeton, NJ, “A System for Automated
Iris Recognition”, Proceedings of the Second IEEE Workshop on Applications of
ComputerVision,1994.


[8] W. 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, April 1998.








6. REFERENCES

(C
NTD
……..)


[9] 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.


[10 ]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.


[11] Y. Zhu, T. Tan, Y. Wang. Biometric personal identification based on
irispatterns
. Proceedings of the 15th International Conference on Pattern
Recognition, Spain, Vol. 2, 2000.


[12] C. Tisse, L. Martin, L. Torres, M. Robert. Person identification technique
using human iris



recognition. International Conference on Vision Interface, Canada, 2002.


[13]Chinese Academy of Sciences


Institute of Automation. Database of 756
Greyscale Eye Images
. http://www.sinobiometrics.com

Version 1.0, 2003.


[14] C. Barry, N. Ritter. Database of 120 Greyscale Eye Images. Lions Eye
Institute, Perth Western Australia.


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

model. Proceedings of 2001 International Symposium on
Intelligent Multimedia, Video and Speech Processing, Hong Kong, 2001.




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