Predicting large population data cumulative match characteristic performance from small population data

Authors:
Amos Y. Johnson;Jie Sun;Aaron F. Bobick
Affiliations:
GVU Center, College of Computing, Georgia Tech, Atlanta, GA;GVU Center, College of Computing, Georgia Tech, Atlanta, GA;GVU Center, College of Computing, Georgia Tech, Atlanta, GA
Venue:
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Year:
2003

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Abstract

Given a biometric feature-space, in this paper we present a method to predict cumulative match characteristic (CMC) curve performance for a large population of individuals using a significantly smaller population to make the prediction. This is achieved by mathematically modelling the CMC curve. For a given biometric technique that extracts measurements of individuals to be used for identification, the CMC curve shows the probability of recognizing that individual within a database of measurements that are extracted from multiple individuals. As the number of individuals in the database increase, the probabilities displayed on the CMC curve decrease, which indicate the decreasing ability of the biometric technique to recognize individuals. Our mathematical model replicates this effect, and allows us to predict the identification performance of a technique as more individuals are added without physically needing to extract measurements from more individuals.