Creating and Maintaining Databases

Dr. Pushkin Kachroo

Enrollment

• Collect Private Information, e.g. fingerprint

• Follow “enrollment policy”

• Policy should be:– acceptable to the public– Clear on how, where and when the private

info will be used

Enrollment Steps

• Positive Enrollment:– Trusted Individuals

– Enrollment Policy EM

– Authentication through:• Seed Documents (Birth Cert., passport)

– Store machine representation of the enrolled in Verification Database M

Enrollment Steps

• Negative Enrollment:– Criminal Identification

– Enrollment Policy EN

– Store machine representation of the enrolled in Screening Database N

General Enrollment

• Target Population: World W

• Ground Truth: legacy databases:– Criminal or civil– Can contain Fake and Duplicate Identities

Fake Identity

• Created Identity– Non-existent person– Biometric screening against criminal

databases might catch the “fake”

• Stolen Identity

The Zoo

• Sheep: – Real world biometric distinctive and stable

• Goats:– Difficult to authenticate

• Lambs:– Enrolled that are easy to imitate (cause passive FA)

• Wolves:– Good at imitating (cause active FA)

• Chameleons:– Easy to imitate and are good at imitating

Sample Quality Control

• Random False Reject/Accept caused by Adverse Signal Acquisition

• Solution– Better User Interface– Better model probabilistic into feature

extraction/matching– Interactively improve input

Quality Control

• Define “desirable”• Quality related to process-ability• Quantify quality to decide action based on the

level of quality, e.g. present info differently, apply image enhancement etc.

• Compromise between convenience and quality– Affects FTE, and also FA and FR

• ROC can be improved by eliminating poor data

ROC-Quality Control

FMR (False Match Rate)

FN

MR

(F

alse

Non

-mat

ch R

ate)

Throw out bad data

Training

• Like Machine Learning

• Relate scores to probability that the biometric matches someone or doesn’t

Training

Testing

Enrollment as System Training

• Assigning IDs to Subjects

• Three possibilities

– Correct

– Someone faking enrolled (duplicate)

– Someone faking unenrolled (fake)

– PD=Prob(duplicate)

– PF=Prob(fake)

Database Integrity

• How well database reflects the truth data• Database duplication: Purge detected

duplicates• PD=FNMRE X PDEA

– Prob of duplicate= Match bet. 2 samples not detected; double enroll

• PF=FMRE X PIA

– Prob of fake enroll= Match bet. 2 samples falsely detected; Impersonation attack

PD-PF

FMR (PF…)

FN

MR

(P

D..

)

Probabilistic Enrollment

• Enrollment Process Goal:– Build access control for from

that are authorized– Likelihood of d_i given stored token B_i

midi ,...,1,

miBd ii ,...,1)|( Prob

WM

Probabilistic Enrollment

• Enrollment Process Goal:– Machine representation of the “real” biometric

• Assumption about score : likelihood that we have the same subject– True if equivalently– .

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),( iii BBss

)|()|( iiii BBTsBBmatchcorrect ProbProb

ii FNMRFMR

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Probabilistic Enrollment..

• For realistic assumptions we need to model the world

• Probabilitycan be approximated unrealistically by

• We need (given biomeric data collected during enrollment, O)

miBd ii ,...,1)|( Prob

),( iii BBss

miOdi ,...,1)|( Prob

Modeling the World-1

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ddOOd ii

i Prob

ProbProbProb

)( idProb Prior probability that subject d_i is present

)(OProb Prior probability that this observation will occur

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Modeling numerator on right is a matter of fitting model to data; rest impractical/impossible

Modeling the World-2

• Cohorts– Models of most similar subjects

• World Modeling:– Reduce cohorts to a single model

Modeling the World-3

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For Cohort Modeling

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Updating Probabilities

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Use of Probabilities

• Accuracy improvements

• Define measure of biometric integrity

• Integrity of different biometrics can be combined etc.

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