What qualities make a feature useful for biometric performance? In prior research, pre-dating the advent of deep learning (DL) approaches to biometric analysis, a strong relationship between temporal persistence, as indexed by the intraclass correlation coefficient (ICC), and biometric performance (Equal Error Rate, EER) was noted. More generally, the claim was made that good biometric performance resulted from a relatively large set of weakly intercorrelated features with high ICC. The present study aimed to determine whether the same relationships are found in a state-of-the-art DL-based eye movement biometric system (``Eye-Know-You-Too''), as applied to two publicly available eye movement datasets. To this end, we manipulate various aspects of eye-tracking signal quality, which produces variation in biometric performance, and relate that performance to the temporal persistence and intercorrelation of the resulting embeddings. Data quality indices were related to EER with either linear or logarithmic fits, and the resulting model R^2 was noted. As a general matter, we found that temporal persistence was an important predictor of DL-based biometric performance, and also that DL-learned embeddings were generally weakly intercorrelated.

翻译：何种特征品质对生物特征识别性能至关重要？在深度学习（DL）方法应用于生物特征分析之前的研究中，已观察到以组内相关系数（ICC）衡量的时间持久性与生物特征识别性能（等错误率，EER）之间存在密切关联。更普遍的观点认为，优异的生物特征识别性能源于一组具有高ICC且相互关联性较弱的特征集合。本研究旨在探究，在应用于两个公开眼动数据集的最先进深度学习眼动生物特征识别系统（"Eye-Know-You-Too"）中，是否仍存在相同的关系模式。为此，我们通过调控眼动信号质量的多个维度以产生生物特征识别性能的变异，并将该性能与生成嵌入向量的时间持久性及互相关联性进行关联分析。数据质量指标通过线性或对数拟合与EER建立关系，并记录所得模型的R^2值。总体而言，研究发现时间持久性是预测深度学习生物特征识别性能的重要指标，同时深度学习所获嵌入向量普遍呈现弱互相关性特征。