通过多个数据集的眼动进行用户识别的广泛研究

一些研究报告称，基于眼动特征的生物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，对通过眼动进行用户识别进行了广泛研究。我们分析了我们的方法对影响识别准确性的几个因素，如刺激物的类型、IVT参数（用于将轨迹分割成固定和抽动）、添加新的特征，如眼动的高阶导数、包含眨眼信息、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、添加高阶导数特征和包含额外的眨眼分类器对识别准确性有积极影响。改进的范围从几个百分点，到其中一个数据集上令人印象深刻的9%的增长。

原文题目：An Extensive Study of User Identification via Eye Movements across Multiple Datasets

原文：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 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 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. The improvements range from a few percentage points, up to an impressive 9 % increase on one of the datasets.