Cargando…

Method to assess the temporal persistence of potential biometric features: Application to oculomotor, gait, face and brain structure databases

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Friedman, Lee, Nixon, Mark S., Komogortsev, Oleg V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5456116/
https://www.ncbi.nlm.nih.gov/pubmed/28575030
http://dx.doi.org/10.1371/journal.pone.0178501
_version_ 1783241175988174848
author Friedman, Lee
Nixon, Mark S.
Komogortsev, Oleg V.
author_facet Friedman, Lee
Nixon, Mark S.
Komogortsev, Oleg V.
author_sort Friedman, Lee
collection PubMed
description 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.
format Online
Article
Text
id pubmed-5456116
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-54561162017-06-12 Method to assess the temporal persistence of potential biometric features: Application to oculomotor, gait, face and brain structure databases Friedman, Lee Nixon, Mark S. Komogortsev, Oleg V. PLoS One Research Article 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. Public Library of Science 2017-06-02 /pmc/articles/PMC5456116/ /pubmed/28575030 http://dx.doi.org/10.1371/journal.pone.0178501 Text en © 2017 Friedman et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Friedman, Lee
Nixon, Mark S.
Komogortsev, Oleg V.
Method to assess the temporal persistence of potential biometric features: Application to oculomotor, gait, face and brain structure databases
title Method to assess the temporal persistence of potential biometric features: Application to oculomotor, gait, face and brain structure databases
title_full Method to assess the temporal persistence of potential biometric features: Application to oculomotor, gait, face and brain structure databases
title_fullStr Method to assess the temporal persistence of potential biometric features: Application to oculomotor, gait, face and brain structure databases
title_full_unstemmed Method to assess the temporal persistence of potential biometric features: Application to oculomotor, gait, face and brain structure databases
title_short Method to assess the temporal persistence of potential biometric features: Application to oculomotor, gait, face and brain structure databases
title_sort method to assess the temporal persistence of potential biometric features: application to oculomotor, gait, face and brain structure databases
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5456116/
https://www.ncbi.nlm.nih.gov/pubmed/28575030
http://dx.doi.org/10.1371/journal.pone.0178501
work_keys_str_mv AT friedmanlee methodtoassessthetemporalpersistenceofpotentialbiometricfeaturesapplicationtooculomotorgaitfaceandbrainstructuredatabases
AT nixonmarks methodtoassessthetemporalpersistenceofpotentialbiometricfeaturesapplicationtooculomotorgaitfaceandbrainstructuredatabases
AT komogortsevolegv methodtoassessthetemporalpersistenceofpotentialbiometricfeaturesapplicationtooculomotorgaitfaceandbrainstructuredatabases