Traditionally, extracting patterns from eye movement data relies on statistics of different macro-events such as fixations and saccades. This requires an additional preprocessing step to separate the eye movement subtypes, often with a number of parameters on which the classification results depend. Besides that, definitions of such macro events are formulated in different ways by different researchers. We propose an application of a new class of features to the quantitative analysis of personal eye movement trajectories structure. This new class of features based on algebraic topology allows extracting patterns from different modalities of gaze such as time series of coordinates and amplitudes, heatmaps, and point clouds in a unified way at all scales from micro to macro. We experimentally demonstrate the competitiveness of the new class of features with the traditional ones and their significant synergy while being used together for the person authentication task on the recently published eye movement trajectories dataset.
翻译:传统上,从眼运动数据中提取模式依赖于固定和编程等不同宏观活动的统计数据。这要求增加一个预处理步骤,将眼运动子类型分开,通常有分类结果所依赖的若干参数。此外,这些宏观事件的定义由不同的研究人员以不同的方式制定。我们建议应用一个新的特征类别来对个人眼运动轨迹结构进行定量分析。基于代数表层学的这一新特征类别使得能够从从从从所有尺度从微量到宏观的坐标和振荡时间序列、热图和点点云等不同视觉模式中提取模式。我们实验性地展示新特征类别与传统特征的竞争力及其重要的协同作用,同时用于最近公布的眼运动轨迹数据集的个人认证任务。