This paper presents an objective evaluation of previously unexplored biometric techniques utilizing patterns identifiable in human eye movements to distinguish individuals. The distribution of primitive eye movement features are compared between eye movement recordings using algorithms based on the following statistical tests: the Ansari-Bradley test, the Mann-Whitney U-test, the two-sample Kolmogorov-Smirnov test, the two-sample t-test, and the two-sample Cramer-von Mises test. Score-level information fusion is applied and evaluated by: weighted mean, support vector machine, random forest, and likelihood ratio. The accuracy of each comparison/jusion algorithm is evaluated, with results suggesting that, on high resolution eye tracking equipment, it is possible to obtain equal error rates of 16.5% and rank-1 identification rates of 82.6% using the two-sample Cramér-von Mises test and score-level information fusion by random forest, the highest accuracy results on the considered dataset.
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机译：本文介绍了以前未开发的生物识别技术利用识别人类眼睛运动的模式，以区分个体。使用基于以下统计测试的算法在眼球运动记录之间比较原始眼球运动特征的分布：Ansari-Bradley测试，Mann-Whitney U-Test，The-Sample Kolmogorov-Smirnov测试，两个样本T -test，两种样本的克拉梅 - von mises测试。分数级信息融合应用和评估：加权平均值，支持向量机，随机林和似然比。评估每个比较/ JUSCENCE算法的准确性，结果表明，在高分辨率的眼睛跟踪设备上，可以使用双样本CRAMÉR-获得16.5％的相同误差率和82.6％的秩-1识别率为82.6％ Von Mises测试和得分级信息融合由随机森林，所考虑的数据集上的最高精度结果。
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