This paper develops a new approach to understand and measure variations in biometric sample quality. We begin with the intuition that degradations to a biometric sample will reduce the amount of identifiable information available. In order to measure the amount of identifiable information, 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 information for a person may be calculated by the relative entropy D(p驴q) between the population feature distribution q and the person''s feature distribution p. The biometric information for a system is the mean D(p驴q) for all persons in the population. In order to practically measure D(p驴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, Fisher linear discriminant (FLD) and ICA based face recognition, with biometric information calculated to be 45.0 bits (PCA), 37.0 bits (FLD), 39.0 bits (ICA) and 55.6 bits (fusion of PCA and FLD features). Based on this definition of biometric information, we simulate degradations of biometric images and calculate the resulting decrease in biometric information. Results show a quasi-linear decrease for small levels of blur with an asymptotic behavior at larger blur.
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