  Quality assessment algorithms can be used to estimate the utility of a
biometric sample for the purpose of biometric recognition. "Error versus
Discard Characteristic" (EDC) plots, and "partial Area Under Curve" (pAUC)
values of curves therein, are generally used by researchers to evaluate the
predictive performance of such quality assessment algorithms. An EDC curve
depends on an error type such as the "False Non Match Rate" (FNMR), a quality
assessment algorithm, a biometric recognition system, a set of comparisons each
corresponding to a biometric sample pair, and a comparison score threshold
corresponding to a starting error. To compute an EDC curve, comparisons are
progressively discarded based on the associated samples' lowest quality scores,
and the error is computed for the remaining comparisons. Additionally, a
discard fraction limit or range must be selected to compute pAUC values, which
can then be used to quantitatively rank quality assessment algorithms.
  This paper discusses and analyses various details for this kind of quality
assessment algorithm evaluation, including general EDC properties,
interpretability improvements for pAUC values based on a hard lower error limit
and a soft upper error limit, the use of relative instead of discrete rankings,
stepwise vs. linear curve interpolation, and normalisation of quality scores to
a [0, 100] integer range. We also analyse the stability of quantitative quality
assessment algorithm rankings based on pAUC values across varying pAUC discard
fraction limits and starting errors, concluding that higher pAUC discard
fraction limits should be preferred. The analyses are conducted both with
synthetic data and with real face image and fingerprint data, with a focus on
general modality-independent conclusions for EDC evaluations. Various EDC
alternatives are discussed as well.
