Method to Assess the Temporal Persistence of Potential Biometric Features: Application to Oculomotor, Gait, Face and Brain Structure Databases
Date
2017-06
Authors
Friedman, Lee
Nixon, Mark S.
Komogortsev, Oleg
Journal Title
Journal ISSN
Volume Title
Publisher
Public Library of Science
Abstract
We introduce the intraclass correlation coefficient (ICC) to the biometric community as an index of the temporal persistence, or stability, of a single biometric feature. It requires, as input, a feature on an interval or ratio scale, and which is reasonably normally distributed, and it can only be calculated if each subject is tested on 2 or more occasions. For a biometric system, with multiple features available for selection, the ICC can be used to measure the relative stability of each feature. We show, for 14 distinct data sets (1 synthetic, 8 eye-movement-related, 2 gait-related, and 2 face-recognition-related, and one brain-structure-related), that selecting the most stable features, based on the ICC, resulted in the best biometric performance generally. Analyses based on using only the most stable features produced superior Rank-1-Identification Rate (Rank-1-IR) performance in 12 of 14 databases (p = 0.0065, one-tailed), when compared to other sets of features, including the set of all features. For Equal Error Rate (EER), using a subset of only high-ICC features also produced superior performance in 12 of 14 databases (p = 0. 0065, one-tailed). In general, then, for our databases, prescreening potential biometric features, and choosing only highly reliable features yields better performance than choosing lower ICC features or than choosing all features combined. We also determined that, as the ICC of a group of features increases, the median of the genuine similarity score distribution increases and the spread of this distribution decreases. There was no statistically significant similar relationships for the impostor distributions. We believe that the ICC will find many uses in biometric research. In case of the eye movement-driven biometrics, the use of reliable features, as measured by ICC, allowed to us achieve the authentication performance with EER = 2.01%, which was not possible before.
Description
Keywords
intraclass correlation eoefficient, temporal persistence, Computer Science
Citation
Friedman, L., Nixon, M. S., & Komogortsev, O. V. (2017). Method to assess the temporal persistence of potential biometric features: Application to oculomotor, gait, face and brain structure databases. PLoS ONE, 12(6), pp. 1–42.
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Rights Holder
© 2017 Friedman et al.
Rights License
This work is licensed under a Creative Commons Attribution 4.0 International License.