Entropy, Biometric

Biometric entropy describes the inherent variability in biometric samples in the population. It can also be understood as the information content of biometric samples is related to many questions in biometric technology. For example, one of the most common biometric questions is that of uniqueness (e.g., “are fingerprints unique?”). Such a measure is important for the performance of biometric system, as a measure of the strength of biometric cryptosystems and for privacy measures. It also is relevant for applications such as biometric fusion, where one would like to quantify the biometric information in each system individually, and the potential gain from fusing the systems. Many approaches have been taken to measure biometric entropy, like Wayman (2004) introduced a statistical approach to measure the separability of Gaussian feature distributions using a “cotton ball model”. Daugman (2003) developed “discrimination entropy” to measure the information content of iris images. This...