Why Temporal Persistence of Biometric Features is so Valuable for Classification Performance

Friedman, Lee
Stern, Hal
Price, Larry R.
Komogortsev, Oleg
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Multidisciplinary Digital Publishing Institute
It is generally accepted that relatively more permanent (i.e., more temporally persistent) traits are more valuable for biometric performance than less permanent traits. Although this finding is intuitive, there is no current work identifying exactly where in the biometric analysis temporal persistence makes a difference. In this paper, we answer this question. In a recent report, we introduced the intraclass correlation coefficient (ICC) as an index of temporal persistence for such features. Here, we present a novel approach using synthetic features to study which aspects of a biometric identification study are influenced by the temporal persistence of features. What we show is that using more temporally persistent features produces effects on the similarity score distributions that explain why this quality is so key to biometric performance. The results identified with the synthetic data are largely reinforced by an analysis of two datasets, one based on eye-movements and one based on gait. There was one difference between the synthetic and real data, related to the intercorrelation of features in real data. Removing these intercorrelations for real datasets with a decorrelation step produced results which were very similar to that obtained with synthetic features.
biometrics performance, temporal persistence, normally distributed features, Computer Science
Friedman, L.; Stern, H.S.; Price, L.R.; Komogortsev, O.V. Why temporal persistence of biometric features, as assessed by the intraclass correlation coefficient, is so valuable for classification performance. Sensors, 20(16), 4555.