Past, Present and Future of Iris Recognition - AFCEA GIS 2015 ADVANCED TECH RESEARCH. 9-24-2015. Past, Present and Future of Iris Recognition (from an academic researcher’s viewpoint)

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GIS 2015 ADVANCED TECH RESEARCH. 9-24-2015.

Past, Present and Future of Iris Recognition

(from an academic researcher’s viewpoint)

Kevin W. Bowyer

Schubmehl-Prein Professor

Chair, Department of CS&E

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Past.

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Daugman

patent

granted

0

50

100

150

200

250

IEEE Xplore: Iris AND Biometric

UAE border

control

begins

Flom & Safir

patent

expires

CAB single

factor

authenticatio

n

Aadhaar

exceeds US

population

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Center for Global

Development report

analyzing India’s

experience with the

Aadhaar program.

Conclusions based

on data from

enrolling 84 million

persons.

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CGD report on iris versus fingerprint:

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Present.

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same, Fresh Look lens. same, AccuVue lens.

with no contact lens. same eye, clear contact.

(images brightened for better visibility)

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VeriEye 4.1

matcher,

NDCLD12

image

dataset

(available).

social impact issue

security issue

classic Daugman

performance point

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Detecting lenses of the same brand

as used in the training data is easy.

Detecting brands of lenses not seen

in the training data is more difficult,

can have highly variable results.

Doyle (IEEE Access, 2015) shows

that more brands in the training data

improves generalization.

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The scores are NOT from 1:N search.

They are from “1-to-First” search.

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Problem in “1-to-First” Matching:

False Match can be taken for aTrue Match !

“I am

enrolled”

. . . incorrect

match,

above

threshold

correct

match; min

below

threshold

incorrect

match,

above

threshold

incorrect

match,

below

threshold

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False match frequency in 1-to-First

results from interaction of factors:

number of enrolled irises.

threshold for accepting a match.

range of iris rotation allowed.

spread of impostor distribution.

(1:N, 1:First experimental study in progress)

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1:First versus 1:N detail –

The OPS-XING dataset used in IREX

VI has no independent ground truth.

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The (assumed) authentic distribution is not close to Gaussian.

“truncated”

“censored”

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The IREX VI regression analysis is

not matched to the properties of

the OPS-XING dataset.

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Up-to-3-tries image acquisition

1st, 2nd, 3rd match distributions differ!

Should not be mixed in one regression.

(See paper by Czajka in BTAS 2015.)

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The IREX VI OPS-XING dataset is a

composite of many data streams:

69 different kiosks.

11 different locations.

most with US and CA kiosks.

Toronto T1 and Vancouver are two

of the higher-traffic locations.

(See paper by Ortiz in Biometrics Workshop 2015.)

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Would you fit a straight

line to this data?

Would you merge the two datasets, fit a line

to the merged data, and use that as your

best estimate of iris template aging?

How about this data?

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Future.

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Visible-light iris in some instances. (e.g., see Hugo Proenca’s work)

Better sensors, in an HCI sense.

Better segmentation, matching.

More nation-scale applications. (like Somaliland voter registration)

More on gender, race from iris

More research on iris aging.

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Questions ?

Documents

Past, Present and Future of Iris Recognition - AFCEA GIS 2015 ADVANCED TECH RESEARCH. 9-24-2015. Past, Present and Future of Iris Recognition (from an academic researcher’s viewpoint)