iris recognition system as means of unique identification

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Mentor Name Mr. Gulzar Ahmad(ASST. PROF.)

KRISHNA NAND MISHRARAHUL VASHISHTJASWANT KUMARVIPIN KUMAR

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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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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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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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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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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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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Stages

[2]

Stages of iris based recognition algorithm

Stage 1Image Acquisition:

The purpose of this stage is to capture a high-quality image of the eye.

9System for active iris recognition by IrisScan

System for passive iris recognition by Sensar

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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Stage 3Iris 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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Stage 4Feature Extraction:In this stage, we generate a template code along with a mask code.

Stage 5Pattern matching:Compare two iris templates using Hamming distances.Shifting of Hamming distances: To counter rotational inconsistencies.

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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.

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The project is currently in iris localization phase.

Eye image dataset used is of Chinese University of Hong Kong.

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Some of localized iris images are

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Captured image

[5]

Eye image with circles for localization of iris

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Captured image Eye image with circles for localization of iris

[5]

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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THANK YOU!!!

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