  State-of-the-art appearance-based gaze estimation methods, usually based on
deep learning techniques, mainly rely on static features. However, temporal
trace of eye gaze contains useful information for estimating a given gaze
point. For example, approaches leveraging sequential eye gaze information when
applied to remote or low-resolution image scenarios with off-the-shelf cameras
are showing promising results. The magnitude of contribution from temporal gaze
trace is yet unclear for higher resolution/frame rate imaging systems, in which
more detailed information about an eye is captured. In this paper, we
investigate whether temporal sequences of eye images, captured using a
high-resolution, high-frame rate head-mounted virtual reality system, can be
leveraged to enhance the accuracy of an end-to-end appearance-based
deep-learning model for gaze estimation. Performance is compared against a
static-only version of the model. Results demonstrate statistically-significant
benefits of temporal information, particularly for the vertical component of
gaze.
