Individual Topology Structure of Eye Movement Trajectories

TL;DR

This paper introduces a novel algebraic topology-based feature set for analyzing eye movement trajectories, enabling unified pattern extraction across various modalities and scales, and demonstrates its effectiveness in person authentication.

Contribution

It presents a new class of topological features for eye movement analysis, reducing reliance on macro-event segmentation and improving authentication performance.

Findings

Topological features outperform traditional methods in accuracy.

Features show significant synergy when combined.

Effective across multiple gaze data modalities.

Abstract

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.