User Identification via Free Roaming Eye Tracking Data
Rishabh Vallabh Varsha Haria Amin El Abed Sebastian Maneth
We present a new dataset of "free roaming" (FR) and "targeted roaming" (TR):
a pool of 41 participants is asked to walk around a university campus (FR) or
is asked to find a particular room within a library (TR). Eye movements are
recorded using a commodity wearable eye tracker (Pupil Labs Neon at 200Hz). On
this dataset we investigate the accuracy of user identification using a
previously known machine learning pipeline where a Radial Basis Function
Network (RBFN) is used as classifier. Our highest accuracies are 87.3% for FR
and 89.4% for TR. This should be compared to 95.3% which is the (corresponding)
highest accuracy we are aware of (achieved in a laboratory setting using the
"RAN" stimulus of the BioEye 2015 competition dataset). To the best of our
knowledge, our results are the first that study user identification in a non
laboratory setting; such settings are often more feasible than laboratory
settings and may include further advantages. The minimum duration of each
recording is 263s for FR and 154s for TR. Our best accuracies are obtained when
restricting to 120s and 140s for FR and TR respectively, always cut from the
end of the trajectories (both for the training and testing sessions). If we cut
the same length from the beginning, then accuracies are 12.2% lower for FR and
around 6.4% lower for TR. On the full trajectories accuracies are lower by 5%
and 52% for FR and TR. We also investigate the impact of including higher order
velocity derivatives (such as acceleration, jerk, or jounce).