Open Gaze: Open Source eye tracker for smartphone devices using Deep Learning

TL;DR

This paper introduces an open-source, smartphone-based eye tracker using deep learning that achieves accuracy comparable to expensive hardware, enabling scalable eye movement research and applications in various fields.

Contribution

It presents a novel, accessible eye tracking solution for smartphones that matches state-of-the-art accuracy without additional hardware, leveraging deep learning and the MIT GazeCapture dataset.

Findings

Achieves accuracy similar to high-cost mobile eye trackers

Replicates key ocular motion behavior findings from prior studies

Demonstrates potential for large-scale eye movement research

Abstract

Eye tracking has been a pivotal tool in diverse fields such as vision research, language analysis, and usability assessment. The majority of prior investigations, however, have concentrated on expansive desktop displays employing specialized, costly eye tracking hardware that lacks scalability. Remarkably little insight exists into ocular movement patterns on smartphones, despite their widespread adoption and significant usage. In this manuscript, we present an open-source implementation of a smartphone-based gaze tracker that emulates the methodology proposed by a GooglePaper (whose source code remains proprietary). Our focus is on attaining accuracy comparable to that attained through the GooglePaper's methodology, without the necessity for supplementary hardware. Through the integration of machine learning techniques, we unveil an accurate eye tracking solution that is native to smartphones. Our approach demonstrates precision akin to the state-of-the-art mobile eye trackers, which are characterized by a cost that is two orders of magnitude higher. Leveraging the vast MIT GazeCapture dataset, which is available through registration on the dataset's website, we successfully replicate crucial findings from previous studies concerning ocular motion behavior in oculomotor tasks and saliency analyses during natural image observation. Furthermore, we emphasize the applicability of smartphone-based gaze tracking in discerning reading comprehension challenges. Our findings exhibit the inherent potential to amplify eye movement research by significant proportions, accommodating participation from thousands of subjects with explicit consent. This scalability not only fosters advancements in vision research, but also extends its benefits to domains such as accessibility enhancement and healthcare applications.