EyeWithShut: Exploring Closed Eye Features to Estimate Eye Position

Smart glasses enabling eyes-only input are growing in popularity. The underlying input technique powering the majority of interactions in this modality is dwell-based selection. However, this approach suffers from the Midas touch: the unintentional triggering of input during exploratory gaze. We suggest that multimodal input based on eye motions performed while the eyes are shut can trigger selections and overcome this problem without requiring additional input devices or modalities. To explore this idea, this paper captures a dataset of labeled closed-eye images corresponding to eye motions in various directions from nine participants. This was achieved by recording images from a binocular eye-tracker while participants performed standard eye-targeting tasks with one eye closed: the image from the open eye serves as ground truth for the closed eye's location. To understand the scope of closed-eye input, we explore this data according to three dimensions: the distance (near/far), width (narrow/broad), and direction (horizontal/vertical) of single captures of eye positions. Our results indicate that horizontal and vertical eye positions can be accurately recovered from static closed-eye images, but only after relatively large angular eye movements.