Theoretical Framework for Constructing Matching Algorithms in Biometric Authentication Systems
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Measuring biometric sample quality in terms of biometric feature information in iris images
Journal of Electrical and Computer Engineering
Distance entropy as an information measure for binary biometric representation
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
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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 person’s 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 45.0 bits (PCA), 37.0 bits (FLD) and 55.6 bits (fusion of PCA and FLD features). Finally, we discuss general applications of this measure.