Towards a measure of biometric feature information
This paper develops an approach to measure the information content of a biometric feature representation. We define biometric information as the decrease in uncertainty about the identity of a person due to a set of biometric measurements. We then show that the biometric feature information for a person may be calculated by the relative entropy \({D(p\|q)}\) between the population feature distribution q and the persons feature distribution p. The biometric information for a system is the mean \({D(p\|q)}\) for all persons in the population. In order to practically measure \({D(p\|q)}\) with limited data samples, we introduce an algorithm which regularizes a Gaussian model of the feature covariances. An example of this method is shown for PCA and Fisher linear discriminant (FLD) based face recognition, with biometric feature information calculated to be 45.0bits (PCA), 37.0 bits (FLD) and 55.6 bits (fusion of PCA and FLD features). Finally, we discuss general applications of this measure.
biometric features information content relative entropy face recognition