Synthetic Database for Evaluation of General, Fundamental Biometric Principles
This work provides a tool for studying biometric principles across modalities, but it is incremental as it builds on synthetic data methods without introducing a new paradigm.
The authors tackled the problem of evaluating general biometric principles by creating a synthetic database, finding that temporal persistence variations are highly correlated with biometric performance and that the number of features needed for specific error rates remains constant across database sizes from 100 to 10,000 subjects.
We create synthetic biometric databases to study general, fundamental, biometric principles. First, we check the validity of the synthetic database design by comparing it to real data in terms of biometric performance. The real data used for this validity check was from an eye-movement related biometric database. Next, we employ our database to evaluate the impact of variations of temporal persistence of features on biometric performance. We index temporal persistence with the intraclass correlation coefficient (ICC). We find that variations in temporal persistence are extremely highly correlated with variations in biometric performance. Finally, we use our synthetic database strategy to determine how many features are required to achieve particular levels of performance as the number of subjects in the database increases from 100 to 10,000. An important finding is that the number of features required to achieve various EER values (2%, 0.3%, 0.15%) is essentially constant in the database sizes that we studied. We hypothesize that the insights obtained from our study would be applicable to many biometric modalities where extracted feature properties resemble the properties of the synthetic features we discuss in this work.