FlatTrack: Eye-tracking with ultra-thin lensless cameras
This addresses the problem of bulky eye trackers for AR/VR headsets, offering an incremental improvement in form-factor.
The researchers tackled the bulkiness of wearable eye trackers by developing a compact flat eye gaze tracker using mask-based lensless cameras and a lightweight deep neural network, achieving performance on par with conventional lens-based trackers with real-time operation at over 125 fps.
Existing eye trackers use cameras based on thick compound optical elements, necessitating the cameras to be placed at focusing distance from the eyes. This results in the overall bulk of wearable eye trackers, especially for augmented and virtual reality (AR/VR) headsets. We overcome this limitation by building a compact flat eye gaze tracker using mask-based lensless cameras. These cameras, in combination with co-designed lightweight deep neural network algorithm, can be placed in extreme close proximity to the eye, within the eyeglasses frame, resulting in ultra-flat and lightweight eye gaze tracker system. We collect a large dataset of near-eye lensless camera measurements along with their calibrated gaze directions for training the gaze tracking network. Through real and simulation experiments, we show that the proposed gaze tracking system performs on par with conventional lens-based trackers while maintaining a significantly flatter and more compact form-factor. Moreover, our gaze regressor boasts real-time (>125 fps) performance for gaze tracking.