Individual Topology Structure of Eye Movement Trajectories
This work addresses the challenge of inconsistent and parameter-sensitive preprocessing in eye movement analysis for researchers in fields like psychology or biometrics, though it is incremental as it builds on existing algebraic topology methods.
The authors tackled the problem of extracting patterns from eye movement data by proposing a new class of features based on algebraic topology, which eliminates the need for preprocessing and parameter-dependent classification of macro-events like fixations and saccades. They demonstrated that these features are competitive with traditional methods and show significant synergy when combined for person authentication on a published dataset.
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.