Large Scale Applications Dr. Pushkin Kachroo TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AA

Large Scale Applications To reduce # of matches needed –Filtering: using non-bio like sex, age etc. –Binning: using additional biometric

Size Small: 1000 (approx. stored samples) Medium: 10K Large: 100K Very Large: 1M Extremely Large: 10M

Examples Voter Registration Enrollment Authentication National ID Enrollment Authentication

Fingerprint FAR(1)FRR 1 Finger Fingers

Enrollment False Positives Quadratic ~ [m x FAR(1)] x m For one finger…10^8 gives 10^11 Two finger it gives 10^6

Enrollment False Negatives FNR ~ 10 -3

Authentication Accuracy False Rejects: FRR x m = 10 5 for 10 8 size False Accepts: 10 -5

Matcher Sizing m(t): Database size J(t): Match throughput; #matches needed/time E(t): Enrollment Rate A(t): Authentication Rate

Matcher Throughput

Matcher Throughput-2 National ID Voter Registration/Election

Total # of Matches Verification on Election Day Enrollment

Cost Incremental Cost : Sampling, printing ID, etc.; Linear Exception Handling : Cost of Resolving False Positives; quadratic

Total Cost Even for i = 0 we get:

Cost-Plot Cost(m) m