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

Method to assess the temporal persistence of potential biometric features: application to oculomotor, gait, face and brain structure databases
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.
Fiedman, Lee
6ef39220-9f72-4d7d-bfd8-a14e396ce110
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Komogortsev, Oleg V.
f3f6cbca-6af9-4228-98a6-b4638aab101f
Fiedman, Lee
6ef39220-9f72-4d7d-bfd8-a14e396ce110
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Komogortsev, Oleg V.
f3f6cbca-6af9-4228-98a6-b4638aab101f

Fiedman, Lee, Nixon, Mark S. and Komogortsev, Oleg 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). (doi:10.1371/journal.pone.0178501).

Record type: Article

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.

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Accepted/In Press date: 19 May 2017
e-pub ahead of print date: 2 June 2017
Published date: June 2017
Organisations: Vision, Learning and Control

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Local EPrints ID: 410090
URI: http://eprints.soton.ac.uk/id/eprint/410090
PURE UUID: 9913cc29-7b09-4d3a-a2db-e4c3e4d275c8
ORCID for Mark S. Nixon: ORCID iD orcid.org/0000-0002-9174-5934

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Date deposited: 03 Jun 2017 04:02
Last modified: 07 Oct 2020 06:52

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