Several studies have reported that biometric identification based on eye movement characteristics can be used for authentication. This paper provides an extensive study of user identification via eye movements across multiple datasets based on an improved version of a method originally proposed by George and Routray. We analyzed our method with respect to several factors that affect the identification accuracy, such as the type of stimulus, the Identification by Velocity-Threshold (IVT) parameters (used for segmenting the trajectories into fixation and saccades), adding new features such as higher-order derivatives of eye movements, the inclusion of blink information, template aging, age and gender. We find that three methods namely selecting optimal IVT parameters, adding higher-order derivatives features and including an additional blink classifier have a positive impact on the identification accuracy. When we combine all our methods, we are able to improve the best known accuracy over the BioEye 2015 competition dataset from 86% to 96%.

一些研究报告称，基于眼球运动特征的生物特征识别可用于身份验证。本文基于 George 和 Routray 最初提出的方法的改进版本，通过跨多个数据集的眼球运动对用户识别进行了广泛的研究。我们针对影响识别准确性的几个因素分析了我们的方法，例如刺激的类型、速度阈值识别 (IVT) 参数（用于将轨迹分割成注视和扫视），添加了新的特征，例如更高眼球运动的阶导数，包括眨眼信息、模板老化、年龄和性别。我们发现三种方法，即选择最佳 IVT 参数，添加高阶导数特征并包括额外的眨眼分类器对识别准确性有积极影响。当我们结合我们所有的方法时，我们能够将 BioEye 2015 竞争数据集上最知名的准确率从 86% 提高到 96%。