ESCaF: Pupil Centre Localization Algorithm with Candidate Filtering

TL;DR

This paper introduces ESCaF, a novel pupil localization algorithm that combines edge and intensity data with candidate filtering, achieving high accuracy and real-time performance in challenging real-world conditions.

Contribution

The paper presents a new pupil localization method that improves detection rates in real-world scenarios by integrating edge and intensity information with candidate filtering.

Findings

Outperforms existing algorithms on LPW dataset

Achieves real-time processing speeds

Demonstrates robustness under challenging conditions

Abstract

Algorithms for accurate localization of pupil centre is essential for gaze tracking in real world conditions. Most of the algorithms fail in real world conditions like illumination variations, contact lenses, glasses, eye makeup, motion blur, noise, etc. We propose a new algorithm which improves the detection rate in real world conditions. The proposed algorithm uses both edges as well as intensity information along with a candidate filtering approach to identify the best pupil candidate. A simple tracking scheme has also been added which improves the processing speed. The algorithm has been evaluated in Labelled Pupil in the Wild (LPW) dataset, largest in its class which contains real world conditions. The proposed algorithm outperformed the state of the art algorithms while achieving real-time performance.