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Integrating Information

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.

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Integrating Information

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  1. Integrating Information Dr. Pushkin Kachroo

  2. Integration Match Matcher 1 B1 Integration Decision B2 Matcher 2 No Match

  3. Expanding a Biometric Multiple Biometrics Multiple Fingers Multiple Tokens One Finger Multiple Matchers Multiple Samples Multiple Sensors

  4. Coupling Match Decision Match Decision Integration Integration Process 2 Process 1 Process 2 Process 1 Sensor 1 Sensor 2 Sensor 1 Sensor 2 Loosely Coupled Tightly Coupled

  5. Boolean Combinations Biometric a Accept/Reject AND Biometric b Biometric a Accept/Reject OR Biometric b

  6. Boolean: Convenience/Security Biometric a Accept/Reject AND Biometric b Biometric a Accept/Reject OR Biometric b Improve Convenience: Lower FRR (OR) Improve Security: Lower FAR (AND)

  7. 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’) Tradeoff

  8. 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.

  9. 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.

  10. Dynamic Authentication • Example: Conversational biometric….allows for natural filtering by asking knowledge information during conversation; could include possession; while speaker recognition is taking place.

  11. Boolean: Score Level Integration

  12. Normal Distribution Accept Reject

  13. 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.

  14. Distance based B and Bm are templates from the same biometric per means person

  15. Degenerate Cases

  16. ROC based Methods Mismatch Match Compare to… FM FNM T

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