Applications of Iris Recognition

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Wel-Come To

Next Generation Of security

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Image Processing in

Bio-medical Field Introduces

Presented By:

Miss. Jain Chetna A. Mr. Kothari Abhijeet D.Guided By :

Dr.Mrs.S.D.Apte

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Image Processing• Analysis of Graphical Images.• Three Steps of Image Processing:

Input :- converts colors/shades into binary values.Processing :- Image Enhancement, Normalization,

editing, filtering & data compression.Output: display/printing processed image.

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IRIS Recognition• Protected Internal Organ, Stable Throughout the Life.• Iris Causes the muscles compression or expansion, as

pupil responds to changing lighting condition.• Details of each Iris Phenotypically Unique• Living Passport/ Living Password.• Real Time, Recognition decisions made with High

Confidence even for Large Databases.• Developed at Cambridge University by Dr. John

Daugman.

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How It Works• User stands 1 meter from system.• Contains 3 video cameras. • 2 wide angle cameras for torso • 3rd focuses on eye.• Cameras uses visible & Infrared

(700-900nm) light.• Circular grid is used as a guide to

encode the pattern.

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• Grid is overlaid on eyes image.• Looks at pattern of light/dark

iris area • 512 byte human bar code for

that person is created.• This file size is small.• System compares the bar code

with the version stored in a computer database.

• Complete process takes about 2 seconds.

• Hamming Distance: II(code A X-OR code B)∩mask A∩mask BII

II mask A∩ mask BII

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Eyes & Their Bar Patterns: Here Black Color Represents ‘Logic 1 ‘

whereas White Color Represents ‘Logic 0’.

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Comparison

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Hough Transforms

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Advantages Highly protected internal organ of the eye. 100% Accuracy and Reliability Uniqueness: set by combinatorial complexity. No effect of using contact lenses. Patterns apparently stable throughout life. Encoding and decision-making are tractable

Image analysis and encoding time: 1 second Decidability Index = 7.3 to 11.4 seconds. Search speed: 100,000 Iris Codes per second on

300MHz CPU.

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Disadvantages Small target (1 cm) to acquire from a distance (1 m). Obscured by eyelashes, lenses, reflections. Partially occluded by eyelids. Deforms non-elastically as pupil changes size. Fake detection by “Eye Transplantation”.

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Famous Example

The Most Well-known & proved Example of missing Afghani Girl

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Current & Future Applications

For computer login & telephone call charging. Secure access to bank, Cash machines accounts National border controls, Anti-terrorism Ticket less, Document-free, Air travel. Premises access control, Internet security Driving licenses, personal certificates. Credit-card authentication Secure financial transactions (e-commerce, banking). Automobile ignition and unlocking; anti-theft devices

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Existing Applications

Password Authenticity Over Computer Networks

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Banking System

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Conclusion

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References• Image Processing By: A. K. Jain.• www.whatis.com• www.google.com• www.cl.cam.ac.uk• www.accessexcellence.org• www.melia.com• www.iridiantech.com• www.eyeticket.com• www.nei.nih.gov• www.webvision.med.utah.edu• IEEE Report :Detecting Faces in Images: By Ming-

Hsuan Yang, David J. Kriegman & Narendra Ahuja.

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