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

Published

January 31, 2018

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, US

Conference Title

The International Joint Conference on Biometrics (IJCB 2017)

Pub Type

Conferences

Download Paper

DOI Link

Citation

Tabassi, E. , Tang, L. and Zhu, X. (2018), Repeatability and Reproducibility of Forensic Likelihood Ratio Methods when Sample Size Ratio Varies, The International Joint Conference on Biometrics (IJCB 2017), Denver, CO, US, [online], https://doi.org/10.1109/BTAS.2017.8272737 (Accessed December 7, 2024)