  Modeling eye movement indicative of expertise behavior is decisive in user
evaluation. However, it is indisputable that task semantics affect gaze
behavior. We present a novel approach to gaze scanpath comparison that
incorporates convolutional neural networks (CNN) to process scene information
at the fixation level. Image patches linked to respective fixations are used as
input for a CNN and the resulting feature vectors provide the temporal and
spatial gaze information necessary for scanpath similarity comparison.We
evaluated our proposed approach on gaze data from expert and novice dentists
interpreting dental radiographs using a local alignment similarity score. Our
approach was capable of distinguishing experts from novices with 93% accuracy
while incorporating the image semantics. Moreover, our scanpath comparison
using image patch features has the potential to incorporate task semantics from
a variety of tasks
