An Integrated Prediction Model for Biometrics

Abstract

This paper addresses the problem of predicting recognition performance on a large population from a small gallery. Unlike the current approaches based on a binomial model that use match and non-match scores, this paper presents a generalized two-dimensional model that integrates a hypergeometric probability distribution model explicitly with a binomial model. The distortion caused by sensor noise, feature uncertainty, feature occlusion and feature clutter in the gallery data is modeled. The prediction model provides performance measures as a function of rank, population size and the number of distorted images. Results are shown on NIST-4 fingerprint database and 3D ear database for various sizes of gallery and the population.KeywordsSimilarity ScoreRecognition PerformanceDistorted ImageDistortion ModelBiometric TemplateThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.