  Several studies have reported that biometric identification based on eye
movement characteristics can be used for authentication. This paper provides an
extensive study of user identification via eye movements across multiple
datasets based on an improved version of method originally proposed by George
and Routray. We analyzed our method with respect to several factors that affect
the identification accuracy, such as the type of stimulus, the IVT parameters
(used for segmenting the trajectories into fixation and saccades), adding new
features such as higher-order derivatives of eye movements, the inclusion of
blink information, template aging, age and gender.We find that three methods
namely selecting optimal IVT parameters, adding higher-order derivatives
features and including an additional blink classifier have a positive impact on
the identification accuracy. The improvements range from a few percentage
points, up to an impressive 9 % increase on one of the datasets.
