Method to assess the temporal persistence of potential biometric features: Application to oculomotor, gait, face and brain structure databases

We introduce the intraclass correlation coefficient (ICC) to the biometric community as an index of the temporal persistence, or stability, of a single biometric feature. It requires, as input, a feature on an interval or ratio scale, and which is reasonably normally distributed, and it can only be calculated if each subject is tested on 2 or more occasions. For a biometric system, with multiple features available for selection, the ICC can be used to measure the relative stability of each feature. We show, for 14 distinct data sets (1 synthetic, 8 eye-movement-related, 2 gait-related, and 2 face-recognition-related, and one brain-structure-related), that selecting the most stable features, based on the ICC, resulted in the best biometric performance generally. Analyses based on using only the most stable features produced superior Rank-1-Identification Rate (Rank-1-IR) performance in 12 of 14 databases (p = 0.0065, one-tailed), when compared to other sets of features, including the set of all features. For Equal Error Rate (EER), using a subset of only high-ICC features also produced superior performance in 12 of 14 databases (p = 0. 0065, one-tailed). In general, then, for our databases, prescreening potential biometric features, and choosing only highly reliable features yields better performance than choosing lower ICC features or than choosing all features combined. We also determined that, as the ICC of a group of features increases, the median of the genuine similarity score distribution increases and the spread of this distribution decreases. There was no statistically significant similar relationships for the impostor distributions. We believe that the ICC will find many uses in biometric research. In case of the eye movement-driven biometrics, the use of reliable features, as measured by ICC, allowed to us achieve the authentication performance with EER = 2.01%, which was not possible before.

我们将组内相关系数（intraclass correlation coefficient, ICC）引入生物特征识别领域，将其作为单一生物特征的时间持久性（或称稳定性）指标。该方法要求输入特征取自区间或比率量表，且需满足近似正态分布，且仅当每个受试者接受过2次及以上测试时方可计算。对于具备多个可选特征的生物特征识别系统，ICC可用于衡量各特征的相对稳定性。

我们针对14个不同数据集开展实验，其中包含1个合成数据集、8个眼动相关数据集、2个步态相关数据集、2个人脸识别相关数据集以及1个脑结构相关数据集。实验结果表明，基于ICC选取稳定性最高的特征，通常可获得最优的生物特征识别性能。与其他特征集（包括全特征集）相比，仅使用最稳定特征进行分析时，14个数据库中有12个实现了更优的首识别率（Rank-1-Identification Rate, Rank-1-IR）表现（单尾检验p=0.0065）。对于等错误率（Equal Error Rate, EER），仅使用高ICC特征子集同样在14个数据库中的12个实现了更优性能（单尾检验p=0.0065）。

总体而言，在我们所使用的数据库中，预先筛选潜在生物特征、仅选择高可靠性特征的策略，相较于选择低ICC特征或合并所有特征的策略，可获得更优的识别性能。我们还发现：随着一组特征的ICC值升高，真实相似度分数分布的中位数会上升，而该分布的离散程度会降低；但冒名顶替者分数分布并未出现此类具有统计学显著性的相似关联。

我们认为，ICC将在生物特征识别研究中得到广泛应用。以眼动驱动的生物特征识别为例，通过使用经ICC衡量的可靠特征，我们得以实现EER=2.01%的认证性能，而此前无法达到该水平。