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

• Published in: PloS one Vol. 12; no. 6; p. e0178501
• Main Authors: Friedman, Lee, Nixon, Mark S., Komogortsev, Oleg V.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 02.06.2017; Public Library of Science (PLoS)
• 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; United States; United States > US; Texas
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0178501

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.