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Integrating Information. Dr. Pushkin Kachroo. Integration. Match. Matcher 1. B 1. Integration. Decision. B 2. Matcher 2. No Match. Expanding a Biometric. Multiple Biometrics. Multiple Fingers. Multiple Tokens. One Finger. Multiple Matchers. Multiple Samples. Multiple Sensors. - PowerPoint PPT Presentation
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Integrating Information
Dr. Pushkin Kachroo
Integration
Matcher 1
Matcher 2
Integration Decision
Match
No Match
B1
B2
Expanding a Biometric
Multiple Matchers
Multiple Biometrics
Multiple Fingers
Multiple Samples
Multiple Sensors
One Finger
Multiple Tokens
Coupling
Sensor 1 Sensor 2
Process 1 Process 2
Integration
Match Decision
Sensor 1 Sensor 2
Process 1 Process 2
Integration
Match Decision
Tightly Coupled Loosely Coupled
Boolean Combinations
Biometric aBiometric b
Accept/RejectAND
Accept/RejectBiometric aBiometric b
babORa FRRFRRFRR }{
bababORa FARFARFARFARFAR
OR
babANDa FARFARFAR }{
bababANDa FRRFRRFRRFRRFRR
Boolean: Convenience/Security
Biometric aBiometric b
Accept/RejectAND
Accept/RejectBiometric aBiometric b
baba FRRFRRFRRFRR
OR
baba FARFARFARFAR
FAR FRR
OR
AND
ba FARFAR
ba FRRFRR baFARFAR
baFRRFRR
Improve Convenience:Lower FRR (OR)
Improve Security:Lower FAR (AND)
Filtering-Binning
Penetration Rate: Ppr: The fraction of database being matched on average
Binning Error Rate: Pbe
• Filtering using non-biometric, e.g. using last name. (P,B)
• Binning using biometric, e.g. some whorl pattern (B,B’)
TradeoffTradeoff
Filtering Error-Negative Identification
• Adding Pn for subject dn to negative identification prescribes narrowing down on a smaller set of biometric template =>
Since we are comparing over a smaller set, the chance of false positives goes down. However, false negatives goes up because you might say the person is not in the database (looking at the smaller set) when the person might be in the full database.
Filtering Error-Positive Identification
• The probability that a person is who she/he says she/he is equals the probability of a match between stored biometric template and a newly acquired biometric sample. This match probability does not change if additional knowledge or possession is supplied.
Dynamic Authentication
• Example: Conversational biometric….allows for natural filtering by asking knowledge information during conversation; could include possession; while speaker recognition is taking place.
Boolean: Score Level Integration
1T as
bs
2T
ANDOR
OR
Normal Distribution
1T as
bs
2T
Accept
Reject
TssssG baba )1(),(
Tssss
ssGba
bbaa
b
b
a
aba
22
22
22),(
Normal Distribution: Problems
• Covariance Matrix is assumed to be diagonal; okay for disparate biometrics but not for similar ones e.g. two fingers.
• Gaussian gives non-zero probability to negative scores.
Distance based
),(1),( mm BBsBBDist
2
2
1exp
2
1)(
m
mEdd
Prob
)),((
)),((
mm
mm
BBDist
BBDistEE
B and Bm are templates from the same biometric
)1)(|()|(
)|()|(
mmmm
mmm PcohortBPperB
PperBBper
ProbProb
ProbProb
per means person
Degenerate Cases
Ts
ssGa
aba
2),(
ba
Ts
ssGb
bba
2),(
ba
as
bs
ROC based Methods
as
bs
Match Mismatch
),(),( baba ssFRRssF ),(1),( baba ssFARssG
Compare to…
)(spn
)(spm
TFNM FM