Creating and Maintaining Databases

1. Creating and Maintaining Databases Dr. Pushkin Kachroo

2. 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

3. 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

4. Enrollment Steps • Negative Enrollment: • Criminal Identification • Enrollment Policy EN • Store machine representation of the enrolled in Screening Database N

5. General Enrollment • Target Population: World W • Ground Truth: legacy databases: • Criminal or civil • Can contain Fake and Duplicate Identities

6. Fake Identity • Created Identity • Non-existent person • Biometric screening against criminal databases might catch the “fake” • Stolen Identity

7. 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

8. 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

9. 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

10. ROC-Quality Control Throw out bad data FNMR (False Non-match Rate) FMR (False Match Rate)

11. Training • Like Machine Learning • Relate scores to probability that the biometric matches someone or doesn’t Training Testing

12. 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)

13. 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

14. PD-PF FNMR (PD..) FMR (PF…)

15. Probabilistic Enrollment • Enrollment Process Goal: • Build access control for from that are authorized • Likelihood of d_i given stored token B_i

16. Probabilistic Enrollment • Enrollment Process Goal: • Machine representation of the “real” biometric • Assumption about score : likelihood that we have the same subject • True if equivalently • .

17. 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)

18. Modeling the World-1 Prior probability that subject d_i is present Prior probability that this observation will occur Modeling numerator on right is a matter of fitting model to data; rest impractical/impossible

19. Modeling the World-2 • Cohorts • Models of most similar subjects • World Modeling: • Reduce cohorts to a single model

20. Modeling the World-3 For Cohort Modeling

21. Updating Probabilities

22. Use of Probabilities • Accuracy improvements • Define measure of biometric integrity • Integrity of different biometrics can be combined etc.