Likelihood-ratio-based biometric verification

This paper presents results on optimal similarity measures for biometric verification based on fixed-length feature vectors. First, we show that the verification of a single user is equivalent to the detection problem, which implies that for single-user verification the likelihood ratio is optimal. Second, we show that under some general conditions, decisions based on posterior probabilities and likelihood ratios are equivalent, and result in the same ROC. However, in a multi-user situation, these two methods lead to different average error rates. As a third result, we prove theoretically that, for multi-user verification, the use of the likelihood ratio is optimal in terms of average error rates. The superiority of this method is illustrated by experiments in fingerprint verification. It is shown that error rates of approximately 10^-4 can be achieved when using multiple fingerprints for template construction.