In general machine learning applications performance evaluation is based on accuracy metrics, that are based on correct versus incorrect ratio.
In biometrics is more important to consider the rate of errors than the rate of correct answers.
Counting errors is not enough to evaluate the system, there is also the need to compute the rate between the errors and not-error. We will in fact consider measures like False Rejection Rate and the most critical for security False Acceptance Rate.