The mathematical relationship between the expected confusion metric and the area under a receiver operating characteristic (ROC) curve is derived. Given a limited database of subjects and a recognition technique that generates a feature vector per subject, expected confusion is used to predict how well the feature vector will filter identity in a larger population. Related is the area under a ROC curve that can be used to determine the probability of correctly discriminating between subjects given the feature vector. These two measures have different connotations, but we show mathematically and verify experimentally that a simple transformation can be applied to the expected confusion to find the probability of incorrectly discriminating between subjects, which is the complement of the area under a ROC curve. Furthermore, we show that as a function of the number of subjects, the expected confusion measure converges more quickly than the ROC curve.