Method to Assess the Temporal Persistence of Potential Biometric Features: Application to Oculomotor, and Gait-Related Databases

TL;DR

This study proposes a method to evaluate the temporal persistence of biometric features using test-retest reliability, demonstrating that selecting highly reliable features improves biometric identification performance across multiple datasets.

Contribution

It introduces a standardized approach using ICC to assess feature reliability and shows that high-ICC features enhance biometric recognition accuracy.

Findings

High-ICC features improve Rank-1-IR in 9 of 10 datasets.

High-ICC features improve EER in 8 of 10 datasets.

Prescreening for reliability enhances biometric system performance.

Abstract

Although temporal persistence, or permanence, is a well understood requirement for optimal biometric features, there is no general agreement on how to assess temporal persistence. We suggest that the best way to assess temporal persistence is to perform a test-retest study, and assess test-retest reliability. For ratio-scale features that are normally distributed, this is best done using the Intraclass Correlation Coefficient (ICC). For 10 distinct data sets (8 eye-movement related, and 2 gait related), we calculated the test-retest reliability ('Temporal persistence') of each feature, and compared biometric performance of high-ICC features to lower ICC features, and to the set of all features. We demonstrate that using a subset of only high-ICC features produced superior Rank-1-Identification Rate (Rank-1-IR) performance in 9 of 10 databases (p = 0.01, one-tailed). For Equal Error Rate (EER), using a subset of only high-ICC features produced superior performance in 8 of 10 databases (p = 0.055, one-tailed). In general, then, prescreening potential biometric features, and choosing only highly reliable features will yield better performance than lower ICC features or than the set of all features combined. We hypothesize that this would likely be the case for any biometric modality where the features can be expressed as quantitative values on an interval or ratio scale, assuming an adequate number of relatively independent features.