This document provides an overview of iris recognition technology. It discusses the motivation for using biometric authentication like iris recognition. It then reviews the history of iris recognition, pioneered by Flom and Safir in 1987 and later patented by John Daugman. The document outlines the structure of the eye and iris, and describes the typical stages of iris recognition including image acquisition, iris localization, normalization, feature extraction, and pattern matching. It notes advantages like the uniqueness and stability of iris patterns. Finally, it states the current status of the project, which is in the iris localization phase using an eye image dataset from Chinese University of Hong Kong.

1. 1
Mentor Name
Mr. Gulzar Ahmad(ASST.
PROF.)
KRISHNA NAND MISHRA
RAHUL VASHISHT
JASWANT KUMAR
VIPIN KUMAR

2. Content
1. Motivation
2. History
3. Introduction to Iris Recognition
4. Why Iris Recognition
5. Structure of Eye
6. Stages
7. Advantages
8. Status of Project
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3. Motivation
 Authentication – the process of verifying that a
user requesting a resource is who he, she, or it
claims to be, and vice versa.
 Conventional authentication methods
„something that you have“ – key, magnetic card
or smartcard
„something that you know“ – PIN or password
 Biometric authentication uses personal features
„something that you are“
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4. History
 Iris Recognition system was first proposed
by Flom and Safir in 1987. [1][3][4]
 In the year 1994, John Daugman patented
his "biometrics personal identification
system based on iris analysis"[1][3][4].
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5. INTRODUCTION TO IRIS RECOGNITION
John Daugman, University of
Cambridge – Pioneer in Iris
Recognition.
Sharbat Gula – aged 12 at
Afghani refugee camp.
18 years later at a remote
location in Afghanistan.
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6. Why Iris Recognition?
 Iris patterns are unique.
 Iris patterns do not change with age.
 Non Contact approach.
 Simplicity and ease of implementation.
 Speed – the process of matching the iris
patterns is very fast.
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7. Structure of Eye
 Iris is the area of the eye where the pigmented
or colored circle, usually brown, blue, rings the
dark pupil of the eye.
 The iris is embedded with tiny muscles that
control the amount of light entering into eye
through the pupil.
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8. 8
Stages
[2]
Stages of iris based recognition algorithm

9. Stage 1
Image Acquisition:
The purpose of this stage is to capture a high-quality
image of the eye.
9
System for active iris recognition by IrisScan
System for passive iris recognition by Sensar

10. Stage 2
Iris Localization:
The Purpose of this stage to localize that
portion of the acquired image that corresponds
to an iris.
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11. Stage 3
Iris Normalization:
The normalisation process will produce iris regions, which have
the same constant dimensions, so that two photographs of the
same iris under different conditions will have characteristic
features.
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12. Stage 4
Feature Extraction:
In this stage, we generate a template code along with a
mask code.
Stage 5
Pattern matching:
Compare two iris templates using Hamming
distances.Shifting of Hamming distances: To counter
rotational inconsistencies.
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13. Advantages
 Uniqueness of iris patterns hence improved
accuracy.
 Highly protected, internal organ of the eye
 Stability : Persistence of iris patterns.
 Non-invasive : Relatively easy to be
acquired.
 Speed : Smaller template size so large
databases can be easily stored and
checked.
 Cannot be easily forged or modified.

14. 14

15.  The project is currently in iris localization phase.
 Eye image dataset used is of Chinese University
of Hong Kong.
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16. Some of localized iris images are
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Captured image
[5]
Eye image with circles
for localization of iris

17. 17
Captured image Eye image with circles
for localization of iris
[5]

18. REFERENCES
[1]J. Daugman , How iris recognition works, IEEE Trans. On Circuits and Systems for Video
Technology., Vol. 14, No. 1, pp. 21-30, January 2004.
[2]Gargi Amoli, Nitin Thapliyal, Nidhi Sethi, “Iris Preprocessing “, International Journal of Advanced
Research in Computer Science and Software Engineering . ,Volume 2, Issue 6, June 2012 ISSN:
2277 128X page 301-304.
[3] J. Daugman, ―High Confidence Visual Recognition of Persons by a Test of Statistical
Independence , IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 15, No.11,
‖
pp.1148-1161, 1993.
[4] John Daugman, ―The importance of being random: statistical principles of iris recognition ,
‖
Pattern Recognition 36 (2003) 279 – 291, 21 December 2001
[5] Dataset, Chinese University of Hong Kong, “http://www.mae.cuhk.edu.hk/~cvl/main_database.htm”
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19. THANK YOU!!!
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