  Eye movements are intricate and dynamic events that contain a wealth of
information about the subject and the stimuli. We propose an abstract
representation of eye movements that preserve the important nuances in gaze
behavior while being stimuli-agnostic. We consider eye movements as raw
position and velocity signals and train separate deep temporal convolutional
autoencoders. The autoencoders learn micro-scale and macro-scale
representations that correspond to the fast and slow features of eye movements.
We evaluate the joint representations with a linear classifier fitted on
various classification tasks. Our work accurately discriminates between gender
and age groups, and outperforms previous works on biometrics and stimuli
clasification. Further experiments highlight the validity and generalizability
of this method, bringing eye tracking research closer to real-world
applications.
