Exploring the upper bound performance limit of iris biometrics using score calibration and fusion

Abstract

Researchers now acknowledge that the ultimate goal for biometric technologies to be error-free may never be achieved for any biometric modality. The key interest therefore for any biometric modality is to know its current performance limits. For the iris modality, which is intensively used for trusted traveller programs in many countries, the question of the iris recognition limitations is of particular importance, as it affects security risk mitigation strategies employed by the programs. In this paper, we provide the answer to this question, based on the recent large-scale evaluations of state-of-the-art iris biometrics systems conducted by the National Institute of Standards and Technology (NIST) and the Canada Border Services Agency (CBSA) and two performance-improving post-processing methods developed by the CBSA and its academic partners: one based on score recalibration and the other based on fusion of decisions from multiple systems. Particular emphasis of the paper is on the description of datasets used in iris evaluations and the presentation of the new large-scale iris dataset created for the purpose at the CBSA. The importance of proper evaluation metrics and methodologies used in iris evaluations, including the subject-based analysis, is discussed.