A metric of identification performance of biometrics based on information content

We propose the minimum distance entropy (MDE) as a metric of biometric information content. The MDE is the probability that two biometric samples correspond exactly expressed in information content and can be calculated through the experiment for interpersonal matching using a set of biometric samples. This metric makes it possible for certain biometrics not only to be compared with other biometrics but also to be partially compared with personal authentication using passwords, PIN, or other methods in regard to the identification performance or the security. In this paper, we discuss the metric in terms of information theory and show how to evaluate it. Then, as an example, we apply it to a fingerprint system and evaluate fingerprint information content through simulations.