$pi_t$- Enhancing the Precision of Eye Tracking using Iris Feature Motion Vectors

TL;DR

This paper introduces a new eye-tracking method that combines iris texture and pupil edge information to significantly improve precision and robustness, enabling better detection of microsaccades and tracking across blinks.

Contribution

The paper presents a novel methodology $pi_t$ that enhances eye-tracking accuracy by integrating iris features and pupil edges, addressing previous limitations like drift and motion blur.

Findings

Improved precision by at least 48% in fixation and tracking tasks.

Successfully identified microsaccades between targets separated by 0.2 degrees.

Enhanced robustness against motion blur and blink-related tracking loss.

Abstract

A new high-precision eye-tracking method has been demonstrated recently by tracking the motion of iris features rather than by exploiting pupil edges. While the method provides high precision, it suffers from temporal drift, an inability to track across blinks, and loss of texture matches in the presence of motion blur. In this work, we present a new methodology $pi_t$ to address these issues by optimally combining the information from both iris textures and pupil edges. With this method, we show an improvement in precision (S2S-RMS & STD) of at least 48% and 10% respectively while fixating a series of small targets and following a smoothly moving target. Further, we demonstrate the capability in the identification of microsaccades between targets separated by 0.2-degree.