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 data for a face image quality assessment scenario, with a focus on general modality-independent conclusions for EDC evaluations.
翻译:质量评估算法可用于估计生物识别样本的效用,以用于生物识别识别。研究人员通常使用“错误与丢弃特征”(EDC)曲线及其曲线上的“偏面积”(pAUC)值,评估此类质量评估算法的预测性能。EDC曲线取决于误差类型(如“假非匹配率”(FNMR))、质量评估算法、生物识别识别系统、每个对应于生物识别样本对的比较集,以及与起始误差对应的比较分数阈值。为计算EDC曲线,基于相关样本的最低质量分数,逐步丢弃比较,并计算剩余比较的误差。此外,必须选择放弃分数限制或范围,以计算可用于定量排名质量评估算法的pAUC值。本文讨论和分析了这种质量评估算法评估的各种细节,包括EDC曲线的一般特性,基于硬性下限和软性上限的pAUC值解释性改进,相对排名的使用,分段线性曲线插值与逐步插值方法的比较,以及将质量分数归一化为[0,100]整数范围。我们还分析了基于不同pAUC丢弃分数限制和起始误差的定量质量评估算法排名的稳定性,得出较高的pAUC丢弃分数限制更应被优选。这些分析既针对合成数据,也针对面部图像质量评估场景下的真实数据进行,着重探讨了EDC评估的一般模态无关结论。