Ali Tauseef, Spreeuwers Luuk, Veldhuis Raymond, Meuwly Didier
Department of Electrical Engineering, Mathematics and Computer Science, University of Twente, 7500 AE, Enschede, The Netherlands.
Netherlands Forensic Institute, Laan van Ypenburg 6, 2497 GB, The Hague, The Netherlands.
Sci Justice. 2015 Dec;55(6):499-508. doi: 10.1016/j.scijus.2015.05.003. Epub 2015 Jun 3.
Recently, in the forensic biometric community, there is a growing interest to compute a metric called "likelihood-ratio" when a pair of biometric specimens is compared using a biometric recognition system. Generally, a biometric recognition system outputs a score and therefore a likelihood-ratio computation method is used to convert the score to a likelihood-ratio. The likelihood-ratio is the probability of the score given the hypothesis of the prosecution, Hp (the two biometric specimens arose from a same source), divided by the probability of the score given the hypothesis of the defense, Hd (the two biometric specimens arose from different sources). Given a set of training scores under Hp and a set of training scores under Hd, several methods exist to convert a score to a likelihood-ratio. In this work, we focus on the issue of sampling variability in the training sets and carry out a detailed empirical study to quantify its effect on commonly proposed likelihood-ratio computation methods. We study the effect of the sampling variability varying: 1) the shapes of the probability density functions which model the distributions of scores in the two training sets; 2) the sizes of the training sets and 3) the score for which a likelihood-ratio is computed. For this purpose, we introduce a simulation framework which can be used to study several properties of a likelihood-ratio computation method and to quantify the effect of sampling variability in the likelihood-ratio computation. It is empirically shown that the sampling variability can be considerable, particularly when the training sets are small. Furthermore, a given method of likelihood-ratio computation can behave very differently for different shapes of the probability density functions of the scores in the training sets and different scores for which likelihood-ratios are computed.
最近，在法医生物识别领域，当使用生物识别系统比较一对生物特征样本时，计算一种称为“似然比”的指标的兴趣日益浓厚。一般来说，生物识别系统输出一个分数，因此需要使用似然比计算方法将该分数转换为似然比。似然比是在控方假设Hp（两个生物特征样本来自同一来源）下分数的概率除以在辩方假设Hd（两个生物特征样本来自不同来源）下分数的概率。给定一组在Hp下的训练分数和一组在Hd下的训练分数，存在几种将分数转换为似然比的方法。在这项工作中，我们关注训练集中抽样变异性的问题，并进行了详细的实证研究，以量化其对常用的似然比计算方法的影响。我们研究抽样变异性变化的影响：1）对两个训练集中分数分布进行建模的概率密度函数的形状；2）训练集的大小；3）计算似然比的分数。为此，我们引入了一个模拟框架，可用于研究似然比计算方法的几个属性，并量化似然比计算中抽样变异性的影响。实证表明，抽样变异性可能相当大，特别是当训练集很小时。此外，对于训练集中分数的概率密度函数的不同形状以及计算似然比的不同分数，给定的似然比计算方法的表现可能非常不同。
