  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.
