Covariate-Adjusted ROC Curves: An Introduction and Application to Characterizing Hidden Behavior in Biometric Matching System

• Activity Number: 379 - Bias and Interpretability in Biometrics for Forensic Science
• Type: Topic-Contributed
• Date/Time: Thursday, August 12, 2021 : 12:00 PM to 1:50 PM
• Sponsor: Section on Statistics in Defense and National Security
• Author(s): Larry Tang* and Martin Slawski and Xiaochen Zhu
• Companies: University of Central Florida and George Mason University and George Mason University
• Keywords: ROC; accuracy; regression; biometrics

Abstract

The receiver operating characteristic (ROC) curve is widely used to assess discriminative accuracy of two groups based on a continuous score. In a variety of applications, the distributions of such scores across the two groups exhibit a stochastic ordering. We consider modeling of ROC curves using both the order constraint and covariates associated with each score given that the latter (e.g., demographic characteristics of the underlying subjects) often have a substantial impact on discriminative accuracy. The proposed method is based on the indirect ROC regression approach using a location-scale model, and quadratic optimization is used to implement the order constraint. Its practical usefulness is demonstrated in an application to face recognition data from the “Good, Bad, and Ugly” face challenge, a domain in which accounting for covariates has hardly been studied.