iris recognition by john daugman

7/30/2019 iris recognition by john daugman

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Iris Recognition

Following the work of

John DaugmanUniversity of Cambridge


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Biometricsfingerprint face iris DNA

Convenience incapturing fair good good poor

Low intra-classvariance

good fair excellent excellent

High interclassvariance

good good excellent excellent

Difficult to fool good good excellent excellent

Cost effective fair good fair poor


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Properties of the iris Has highly distinguishing texture

Right eye differs from left eye

Twins have different iris texture

Not trivial to capture quality image

+ Works well with cooperative subjects + Used in many airports in the world


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Represent iris texture as a

binary vector of 2048 bits

Representation ofiris and also of aperson

Textured region isunique for aperson


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Find (nearly circular) iris and

create 8 bands or zonesNeed to locate theoverall region of

the iris. Then needto measuretexture in 1024smallneighborhoods;perhaps 128

around each of 8bands.


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Cross correlate 1024 local

areas with a Gabor wavelet

Get 2 bits at eachlocation/orientation.

Threshold the dotproduct of 2 filterswith the iris area.

Polar coordinates

locate the texturepatch.

Filter (mask)has 2 width

parameters


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Use 2nd directional derivative

and 1

st

directional derivative

LOG wave in alphadirection

Gaussian

smoothing inbeta direction


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The directional filters defined

mathematically

sinusoidtaper downin radialdirection

Taper downin tangentialdirection

Image intensity

in polar coords


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Summary of feature extraction Obtain quality image of certain (left) eye

Find boundary of pupil and outside of iris

Normalize radii to range, say, 0.5 to 1.0

Define the 8 bands by radii ranges

Perform 2 dot products at each of 1024locations defined around the bands byradius rho and angle phi


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How is the matching done to

templates of enrolled persons? Person scanned under controlled

environment and iris pattern is stored

with ID (say address, SS#, etc.) Might be several million such templates

for frequent flyers (6B for all world)

At airport or ATM, scan unknownpersons left eye; then computeHamming distance to ALL templates.


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Distributions of true matches

versus non matches

Hammingdistancesof true

matches

Hammingdistancesof falsematches


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Design of former SENSAR ATM

iris scanner


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ecogn on s poss e ycomparing unknown scan to

MILLIONS of stored templates Less than 32% unmatched bits means

MATCH

Only need to count unmatched bits

use exclusive OR with machine words

Mask off bad patches due to eyelid or

eyelash interference (have to detectthat)

iris recognition by john daugman

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