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If you have a target level of biometric performance (e.g. EER = 5% or 0.1%),how many units of unique information (uncorrelated features) are needed toachieve that target? We show, for normally distributed features, that theanswer to that question depends on the temporal persistence of the feature set.We address these questions with synthetic features introduced in a priorreport. We measure temporal persistence with an intraclass correlationcoefficient (ICC). For 5 separate EER targets (5.0%, 2.0%, 1.0%, 0.5% and 0.1%)we provide linear relationships between the temporal persistence of the featureset and the log10(number of features). These linear relationships will helpthose in the planning stage, prior to setting up a new biometric system,determine the required temporal persistence and number of independent featuresneeded to achieve certain EER targets.

The Linear Relationship between Temporal Persistence, Number of Independent Features and Target EER