1. Integrating Information Dr. Pushkin Kachroo

2. Integration Matcher 1 Matcher 2 Integration Decision Match No Match B1B1 B2B2

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

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

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

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

7. Filtering-Binning  Penetration Rate: P pr : The fraction of database being matched on average  Binning Error Rate: P be 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 P n for subject d n 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 B m are templates from the same biometric per means person

15. Degenerate Cases

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