The Linear Relationship between Temporal Persistence, Number of Independent Features and Target EER
This work helps planners in biometric system design by providing quantitative guidelines, but it is incremental as it builds on prior synthetic feature methods.
The paper tackles the problem of determining the number of independent features needed to achieve specific biometric performance targets (EER levels), showing that this depends on the temporal persistence of the features, with linear relationships provided for five EER targets.
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 to achieve that target? We show, for normally distributed features, that the answer to that question depends on the temporal persistence of the feature set. We address these questions with synthetic features introduced in a prior report. We measure temporal persistence with an intraclass correlation coefficient (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 feature set and the log10(number of features). These linear relationships will help those in the planning stage, prior to setting up a new biometric system, determine the required temporal persistence and number of independent features needed to achieve certain EER targets.