  We study the problem of identifying viewers of arbitrary images based on
their eye gaze. Psychological research has derived generative stochastic models
of eye movements. In order to exploit this background knowledge within a
discriminatively trained classification model, we derive Fisher kernels from
different generative models of eye gaze. Experimentally, we find that the
performance of the classifier strongly depends on the underlying generative
model. Using an SVM with Fisher kernel improves the classification performance
over the underlying generative model.
