On Some Performance Indices for Biometric Identification System

This paper investigates a new approach to formulate performance indices of biometric system using information theoretic models. The performance indices proposed here (unlike conventionally used FAR, GAR, DET etc.) are scalable in estimating performance of large scale biometric system. This work proposes a framework for identification capacity of a biometric system, along with insights on number of cohort users, capacity enhancements from user specific statistics etc. While incorporating feature level information in a rate-distortion framework, we derive condition for optimal feature representation. Furthermore, employing entropy measures to distance (hamming) distribution of the encoded templates, this paper proposes an upper bound for false random correspondence probability. Our analysis concludes that capacity can be the performance index of a biometric system while individuality expressed in false random correspondence can be the performance index of the biometric trait and representation. This paper also derives these indices and quantifies them from system parameters.