Eye tracking is used in smart eye-wear to understand the gaze direction of the users’ eyes and must be accurate and with low-latency for pleasant user experience, and low-power for extended use time in a mobile device. However, visual eye tracking using traditional cameras is not ideal given the typical dynamics of eye-motion. The fixed sample rate (fps) is either under-sampling (lower accuracy) during fast eye motion during a saccade or oversampling (wasted computation) during gaze fixations when the eye-ball pose is stationary. An alternative technology, event-cameras, respond only to lighting change and therefore the visual signal adapts to the changing eye dynamics. High-frequency tracking can be achieved during saccades, while redundant processing is avoided during stationary gaze. In addition, due to small data packets, low-latency is also achieved. We present an event-camera-only eye-tracking proof-of-concept using a 5-DoF deformation model and demonstrate strong potential for accurate, low-latency (400 Hz) eye-tracking on a publicly available dataset; demonstrating the first gaze direction (pitch and yaw) estimation with an event-camera. We highlight the challenges still required to be solved to bring event camera technology to commercial viability for eye-tracking.