IT and SLT characterizations of secured biometric authentication systems

Abstract

This paper provides an information theoretical description of biometric systems at the system level. A number of basic models to characterize performance of biometric systems are presented. All models compare performance of an automatic biometric recognition system against performance of an ideal biometric system that knows correct decisions. The correct decision can be visualized as an input to a new decision system, and the decision by an automatic recognition system is the output of this decision system. The problem of performance evaluation for a biometric recognition system is formulated as (1) the problem of finding the maximum information that the output of the system has about the input, and (2) the problem of finding the maximum distortion that the output can experience with respect to the input of the system to guarantee a bounded average probability of recognition error. The first formulation brings us to evaluation of capacity of a binary asymmetric and M-ary channels. The second formulation falls under the scope of rate-distortion theory. We further describe the problem of physical signature authentication used to authenticate a biometric acquisition device and state the problem of secured biometric authentication as the problem of joint biometric and physical signature authentication. One novelty of this work is in restating the problem of secured biometric authentication as the problem of finding capacity and rate-distortion curve for a secured biometric authentication system. Another novelty is in application of transductive methods from statistical learning theory to estimate the conditional error probabilities of the system. This set of parameters is used to optimize the system performance.