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Repeatability and Reproducibility of Forensic Likelihood Ratio Methods when Sample Size Ratio Varies

Published

Author(s)

Elham Tabassi, Larry Tang, Xiaochen Zhu

Abstract

Existing statistical methods for estimating the log- likelihood ratio from biometric scores include parametric estimation, kernel density estimation, and recently adopted logistic regression estimation. There has been a growing in- terest to study the repeatability and reproducibility of these methods on biometric datasets after the 2009 National Re- search Council report and the more recent report from the 2016 President’s Council of Advisors on Science and Tech- nology. For a statistical forensic evaluation method to be repeatable, it needs to generate consistent log-likelihood ratio scores for various sample size ratios between the genuine (mated) and imposter (non-mated) scores computed using the same database. It is a well known fact, that for logistic regression methods, the estimated intercept value depends on the sample size ratio between the two groups. There- fore, when computing log-likelihood ratios using logistic regression estimation, different genuine and impostor sample size ratios could result in different log-likelihood ratio Values. We performed extensive simulations and used different face and fingerprint biometric datasets to investigate the repeatability and reproducibility of the existing log-likelihood ratio estimation methods.
Conference Dates
October 1-4, 2017
Conference Location
Denver, CO
Conference Title
The International Joint Conference on Biometrics (IJCB 2017)
Created February 1, 2018, Updated November 10, 2018