  We study the problem of identifying individuals based on their characteristic
gaze patterns during reading of arbitrary text. The motivation for this problem
is an unobtrusive biometric setting in which a user is observed during access
to a document, but no specific challenge protocol requiring the user's time and
attention is carried out. Existing models of individual differences in gaze
control during reading are either based on simple aggregate features of eye
movements, or rely on parametric density models to describe, for instance,
saccade amplitudes or word fixation durations. We develop flexible
semiparametric models of eye movements during reading in which densities are
inferred under a Gaussian process prior centered at a parametric distribution
family that is expected to approximate the true distribution well. An empirical
study on reading data from 251 individuals shows significant improvements over
the state of the art.

Machine Learning