Automatic Acquisition of a 3D Eye Model for a Wearable First-Person Vision Device

Abstract

A wearable gaze tracking device can work with users in daily-life. For long time of use, a non-active method that does not employ an infrared illumination system is desirable from safety standpoint. It is well known that the eye model constraints substantially improve the accuracy and robustness of gaze estimation. However, the eye model needs to be calibrated for each person and each device. We propose a method to automatically build the eye model for a wearable gaze tracking device. The key idea is that the eye model, which includes the eye structure and eye-camera relationship, impose constraints on image analysis even when it is incomplete, so we adopt an iterative eye model building process with gradually increasing eye model constraints. Performance of the proposed method is evaluated in various situations, including different eye colors of users and camera configurations. We have confirmed that the gaze tracking system using our eye model works well under general situations: indoor, outdoor and driving scene.