Allowing good impostors to test

Biometric testing should attempt to report unbiased, real-world system performance, especially when tested on limited databases. Though testing on a standard database, such as the Linguistic Data Consortiums\´s YOHO, allows comparison of speaker verification systems, it is well known that certain procedures bias the results low. One such procedure concerns the use of cohort or reference speakers to perform verification, where the cohort speakers are removed as candidate impostors. A method of testing is proposed to remove this bias by modifying the cohort set for each false acceptance test. Results statistically differ for this modified approach, which tries to "best" model the general population with a fixed random sample. Lastly, three techniques to bound the biometric performance, using both parametric and non-parametric resampling is demonstrated.