Modern statistical and philosophical framework for uncertainty assessment in biometric performance testing

The question of estimating uncertainty in measurement is fundamental to all scientific fields. In the field of automated human recognition, lack of repeatability and reproducibility of measurements has been noted since at least the 1970s. This study discusses current approaches to estimation of measurement uncertainty within the broader context of scientific philosophy and measurement science. The authors discuss the Duhem-Quine thesis on testing holism and international standards on estimating and reporting uncertainty in laboratory measurements, then apply these concepts to the estimation of uncertainty in technology, scenario and operational testing in biometrics. The authors advocate for moving beyond the calculation of `coverage´ intervals as defined in the ISO/IEC `guidelines for the expression of uncertainty in measurement´ to full application of the concepts of uncertainty assessment.