Error rate equations for the general biometric system

We derive equations for false-match and false-nonmatch error-rate prediction for the general M-to-N biometric identification system, under the simplifying, but limiting, assumption of statistical independence of all errors. For systems with large N, error rates are shown to be linked to the hardware processing speed through the system penetration coefficient and the throughput equation. These equations are somewhat limited in their ability to handle sample-dependent decision policies and are shown to be consistent with previously published cases for verification and identification. Applying parameters consistent with the Philippine Social Security System benchmark test results for AFIS vendors, we establish that biometric identification systems can be used in populations of 100 million people. Development of more generalized equations, accounting for error correlation and general sample-dependent thresholds, establishing confidence bounds, and substituting the inter-template for the impostor distribution under the template generating policy remain for future study.