The University of Southampton
University of Southampton Institutional Repository

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

Data from: Method to assess the temporal persistence of potential biometric features: application to oculomotor, gait, face and brain structure databases
Data from: 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 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.,DataForUploadAll initial, raw datasets for all 14 databases,
DRYAD
Friedman, Lee
7ab3a6af-9416-4b35-8ec2-0899420c0cae
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Komogortsev, Oleg V.
ee3c256b-2466-4780-b9db-97418ddda7ed
Friedman, Lee
7ab3a6af-9416-4b35-8ec2-0899420c0cae
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Komogortsev, Oleg V.
ee3c256b-2466-4780-b9db-97418ddda7ed

(2017) Data from: Method to assess the temporal persistence of potential biometric features: application to oculomotor, gait, face and brain structure databases. DRYAD doi:10.5061/dryad.sv0q9 [Dataset]

Record type: Dataset

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.,DataForUploadAll initial, raw datasets for all 14 databases,

Full text not available from this repository.

More information

Published date: 1 January 2017

Identifiers

Local EPrints ID: 448538
URI: http://eprints.soton.ac.uk/id/eprint/448538
PURE UUID: 84ad14f0-80c4-4a41-b3b8-addd0795b454
ORCID for Mark S. Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 26 Apr 2021 16:34
Last modified: 27 Apr 2021 01:32

Export record

Altmetrics

Contributors

Contributor: Lee Friedman
Contributor: Mark S. Nixon ORCID iD
Contributor: Oleg V. Komogortsev

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×