Gaze tracking based on pupil estimation using multilayer perception

Abstract

Most accurate gaze trackers commonly use near IR (infrared ray) illuminators to detect a pupil rather than an iris because the pupil detection provides higher accuracy for implementing a gaze tracker and it is easier to detect the pupil under IR illumination. However, the active IR illuminating methods directly emit energies to human eyes and also generate heats to an embedded mobile device. Thus, it may be uncomfortable and unstable to utilize an active IR illuminating method in an embedded mobile device as a gaze tracker for a long time. In this paper, we propose a new gaze tracking method using a common USB camera, in which a multilayer perceptron is applied to estimate the pupil's location using iris area information localized in a face area detected from a captured image. The pupil location information as teaching target signals for the neural network is obtained from off-line experiments using an IR camera with an illuminator. And localized iris area information obtained from on-line experiments using a common USB camera is used as input signals of the neural network. Experimental results show that the proposed method plausibly performs the pupil estimation by the multilayer perceptron and successfully generates gaze tracking by an additional calibration process.