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...

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Bibliographic Details
Published in:	PloS one 2017-06, Vol.12 (6), p.e0178501-e0178501
Main Authors:	Friedman, Lee, Nixon, Mark S, Komogortsev, Oleg V
Format:	Article
Language:	eng
Subjects:	Aging Analysis Biology and Life Sciences Biometrics Biometry Brain Brain research Communities Computer science Control Correlation Correlation coefficient Data bases Data processing Database Management Systems Datasets Eye Eye movements Face Face recognition Feature recognition Gait Humans International conferences Mathematical analysis Medicine and Health Sciences Methods Normal distribution Pattern recognition Permissible error Physical Sciences Physiological aspects Research and Analysis Methods Scale (ratio) Similarity Social Sciences Stability Statistical analysis Temperature Weightlifting
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Summary:	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.
ISSN:	1932-6203 1932-6203