Pattern recognition IRIS recognition

LUCTUER BY
RAFIDH HAMAD KHALAF
IRIS RECOGNITION
By
RAFIDH

IRIS Introduction ?
History of Iris Recognition?
What the iris ?
 Why iris ?
Methods of iris recognition system ?
Image Acquisition ?
Segmentation ?
Normalization ?
Applications ?
Overview:

Iris recognition is a method
of biometric identification
and authentication that use
pattern-recognition
techniques based on high
resolution images of the
irises of an individual's eyes
It is considered to be the most accurate
biometric technology available today.
Introduction

History of Iris Recognition
1997-1999
1987
1987
1980
The concept of Iris Recognition was first proposed by
Dr. Frank Burch in 1939.
It was first implemented in 1990
when Dr. John Daugman created the
algorithms for it.
These algorithms employ methods
of pattern recognition and some
mathematical calculations for iris
recognition.

Iris is the area of the eye where the
pigmented or colored circle,usually
brown, blue, rings the dark pupil of the
eye.
IRIS
Normal Eye Example of iris

What is Iris ?
The colored ring around the pupil
of the eye is called the Iris

What is Iris
?
 The iris is a thin circular diaphragm, which lies
between the cornea and the lens of the human eye.
 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.
 The average diameter of the iris is 12 mm, and the
pupil size can vary from 10% to 80% of the iris
diameter.

Externally visible highly protected internal organ.
Unique patterns.
Not genetically connected unlike eye color.
Stable with age.
Impossible to alter surgically.
Living Password, Can not be forgotten or copied.
Works on blind person.
User needs not to touch appliances.
Accurate , faster , and supports large data base.
What is Iris ?

Methods Of IRIS Recognition System
 In identifying one’s iris, there are 2
methods for its recognition and are:
 Active
 Passive
The active Iris system requires that a user be
anywhere from six to fourteen inches away from the
camera.
The passive system allows the user to be anywhere
from one to three feet away from the camera that
locates the focus on the iris.

Iris Recognition Diagram
Image
Acquisition
Image
Acquisition
Image
Acquisition
Iris
Segmentation Normalization
Feature
Encoding
Feature
Matching
Feature points in the
iris region
IrisTemplates
Database
Identify or Reject
Subject
Iris
Segmentation
Normalization
Feature
Encoding
Identify or Reject
Subject
IrisTemplates
Database
Eye Image Iris Region
IrisTemplate

Image Acquisition
 The first step, image acquisition
deals with capturing sequence of iris
images from the subject using
cameras and sensors with High
resolution and good sharpness.
 These images should clearly show
the entire eye especially iris and
pupil part, and then some
preprocessing operation may be
applied to enhance the quality of
image e.g. histogram equalization,
filtering noise removal etc.
(CASIA) eye image database

Pattern recognition IRIS recognition

The first stage of iris
segmentation to isolate the
actual iris region in a digital
eye image.
The iris region, can be
approximated by two
circles, one for the iris/sclera
boundary and another,
interior to the first, for the
iris/pupil boundary.
Segmentation/concept

the derivatives in the horizontal direction for
detecting the eyelids, and in the vertical direction for
detecting the outer circular boundary of the iris .
Taking only the vertical gradients for locating the iris
boundary will reduce influence of the eyelids when
performing circular Hough transform.
Segmentation/eyelids

 eyelashes are treated as belonging to two types :
1 -separable eyelashes:
which are isolated in the image .
2-multiple eyelashes:
which are bunched together and overlap in the eye image.
 Eyelids and Eyelashes are the main noise factor in the iris image.
 These noise factors can affect the accuracy of the iris recognition
system.
 After applying circular Hough transform to iris, we are applying linear
Hough transform and we get line detected noise region in the iris
image.
 We have to remove these detected eyelids and eyelashes from the iris
image Thresolding is used for the removal of eyelashes. Then, the
noise free iris image can be available for future use.
Segmentation/eyelash

1- Edge Detector
2- Hough Transform
Double
thresholdin
g
edeg
1- Edge Detector
2- Hough Transform
LINEAR HOUGHTRANSFORM
CIRCULAR HOUGH TRANSFORM
smoothing
Finding
gradient
Segmentation
Diagram

a. Iris and pupil localization: Pupil and Iris are considered as
two circles using Circular Hough Transform
Process of finding the iris in
an image
b. Eye lid detection and Eye lash noise
removal using linear Hough Transform
method
Segmentation( cont…)

Once the iris segmented ,the next stage
transform the iris region so that it has fixed
dimensions in order to allow comparisons.
Since variations in the eye like pupil dilation
and the inconsistence iris normalization is
needed.
Pupil dilation inconsistence iris
Normalization process involves unwrapping the
iris and converting it in to its polar equivalent .
Normalization

 It is done using Daugman’s Rubber sheet model .
 The centre of the pupil was considered as the
reference point, and radial vectors pass through
the iris region .
 .
Normalization ( cont...)

The Chines Academy of Sciences – Institute
of Automation (CASIA) eye image database
contains 756 greyscale eye images with 108
unique eyes or class are taken from two
sessions .
Research’s Database

 . ATMs
 .Computer login: The iris as a living
password.
 · National Border Controls UAE
 · Driving licenses and other personal
certificates.
 · benefits authentication.
 ·birth certificates, tracking missing.
 · Credit-card authentication.
 · Anti-terrorism (e.g.:— suspect
Screening at airports)
 · Secure financial transaction (e-
commerce, banking).
 · Internet security, control of access
to privileged information.
Applications

The Largest National Deployment
Iris recognition border-crossing system in the
United Arab Emirates (UAE)

Accuracy changes with user’s height
,illumination , Image quality etc.
Person needs to be still, difficult to scan
if not co-operated.
Risk of fake Iris lenses.
Alcohol consumption causes
deformation in Iris pattern
Disadvantages

1- cost : expensive
2- Ease of use :- easy.
3- Authentication:- high.
4- Identification :- the iris of any one individual.
5- physiological and/or behavior characteristics :-
the iris is physiological characteristics
6- Ability to applied: . high
7- Community acceptance :- low .
8- Automatic : real time.
9-Life cycle:- does not need update
10- maintenance requirement :- not need to maintenance.
important things

Pattern recognition IRIS recognition

More Related Content

What's hot (20)

Similar to Pattern recognition IRIS recognition (20)

More from Mazin Alwaaly (20)

Recently uploaded (20)

Pattern recognition IRIS recognition