On estimating performance indices for biometric identification

This paper investigates an information theoretic approach for formulating performance indices for the biometric authentication. Firstly, we formulate the constrained capacity, as a performance index for biometric authentication system for the finite number of users. Like Shannon capacity, constrained capacity is formulated using signal to noise ratio which is estimated from known statistics of users' biometric information in the database. Constrained capacity of a user and of biometric system is fixed, given the database and the matching function. Experimental analysis using real palmprint and hand geometry images illustrates use of constrained capacity to estimate: (i) performance gains from the cohort information, (ii) the effective number of user-specific cohorts for a user and for the biometric system, (iii) information content of biometric features, and (iv) the performance of score level fusion rules for multimodal biometric system. Secondly, this paper investigates a rate-distortion framework for formulating false random correspondence probability as performance of a generic biometric. Our analysis concludes that constrained capacity can be a promising addition to performance of a biometric system. Similarly, individuality expressed as false random correspondence probability can be the performance index of a biometric trait.