Fast and Accurate Algorithm for Eye Localization for Gaze Tracking in Low Resolution Images

TL;DR

This paper introduces a fast, two-stage algorithm for accurate iris center localization in low-resolution images, enabling gaze tracking with inexpensive webcams by leveraging geometrical eye features.

Contribution

It presents a novel two-stage method combining convolution and boundary tracing for improved iris localization in low-res images, outperforming existing techniques.

Findings

Outperforms state-of-the-art methods on public datasets

Works effectively with low-cost webcams

Achieves accurate iris localization in challenging conditions

Abstract

Iris centre localization in low-resolution visible images is a challenging problem in computer vision community due to noise, shadows, occlusions, pose variations, eye blinks, etc. This paper proposes an efficient method for determining iris centre in low-resolution images in the visible spectrum. Even low-cost consumer-grade webcams can be used for gaze tracking without any additional hardware. A two-stage algorithm is proposed for iris centre localization. The proposed method uses geometrical characteristics of the eye. In the first stage, a fast convolution based approach is used for obtaining the coarse location of iris centre (IC). The IC location is further refined in the second stage using boundary tracing and ellipse fitting. The algorithm has been evaluated in public databases like BioID, Gi4E and is found to outperform the state of the art methods.