Creating and Maintaining Databases
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Creating and Maintaining Databases Dr. Pushkin Kachroo
description
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: - PowerPoint PPT Presentation
Transcript of Creating and Maintaining Databases
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– .
iiiiii ssFNMRBBd )1(11);|(Prob
),( iii BBss
)|()|( iiii BBTsBBmatchcorrect ProbProb
ii FNMRFMR
iksBBs iki ),(
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
)(
)()|()|(
O
ddOOd ii
i Prob
ProbProbProb
)( idProb Prior probability that subject d_i is present
)(OProb Prior probability that this observation will occur
m
jj
im
jjj
iii
dO
dO
ddO
ddOOd
11
)|(
)|(
)()|(
)()|()|(
Prob
Prob
ProbProb
ProbProbProb
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
)|()|(
)|()|(
i
ii dODO
dOOd
ProbProb
ProbProb
For Cohort Modeling
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ii
ii dODO
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ProbProb
ProbProb
Updating Probabilities
)|()(
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)()|,(),|(
OdO
dO
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ddO
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dO
OO
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ProbProb
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ProbProb
Prob
Prob
Prob
ProbProbProb
)()|()()|()( iiii DDOddOO ProbProbProbProbProb
)(1)( ii dD ProbProb
Use of Probabilities
• Accuracy improvements
• Define measure of biometric integrity
• Integrity of different biometrics can be combined etc.
m
dMI
m
ii
1
)()(
Prob
