It is not uncommon for contemporary biometric systems to have more than one match below the matching
threshold, or to have two or more matches having close matching scores. This is especially true for those that store large
quantities of identities and/or are applied to measure loosely constrained biometric traits, such as in identification from
video or at a distance. Current biometric performance evaluation standards however are still largely based on measuring
single-score statistics such as False Match, False Non-Match rates and the trade-off curves based thereon. Such
methodology and reporting makes it impossible to investigate the risks and risk mitigation strategies associated with not
having a unique identifying score. To address the issue, Canada Border Services Agency has developed a novel modality-agnostic
multi-order performance analysis framework. The framework allows one to analyze the system performance at
several levels of detail, by defining the traditional single-score-based metrics as Order-1 analysis, and introducing Order-
2 and Order-3 analysis to permit the investigation of the system reliability and the confidence of its recognition decisions.
Implemented in a toolkit called C-BET (Comprehensive Biometrics Evaluation Toolkit), the framework has been applied
in a recent examination of the state-of-the art iris recognition systems, the results of which are presented, and is now
recommended to other agencies interested in testing and tuning the biometric systems.