Аннотация:
In recent years, there has been an increase in
interest in biometrics research involving the use of brain
characteristics commonly known as behavioral traits.
Human eyes contain a rich source of idiosyncratic
information which may be used for the recognition of an
individual’s identity. This article implements an innovative
experiment and a new approach to processing human eye
movements, ultimately aimed at biometric identification of
individuals. In our experiment, the subjects observe special
test visual stimuli, which are generated on the computer
monitor screen. The eye movements are tracked in
dynamics providing information for constructing a
nonparametric nonlinear dynamic model (Volterra model)
of a human’s oculomotor system (OMS) in the form of
multivariate transient functions.
The implemented method treats eye trajectories as 2-D
distributions of points on the “Coordinate-Time" plane. The
efficiency of dynamic characteristics for personality
identification is confirmed by examples of models built on
the basis of data from real experiments. The resulting OMS
models are a source of information for the selection of
informative features, in the space of which the decisive rule
of optimal identification of individuals is determined using
machine learning methods. Promising results at the task of
identification according to behavioral characteristics of an
individual have been obtained - recognition accuracy is
higher than 97%.
