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

• URL: http://arxiv.org/abs/2308.13495v3
• Date: Wed, 4 Sep 2024 15:12:03 GMT
• Title: Open Gaze: Open Source eye tracker for smartphone devices using Deep Learning
• Authors: Sushmanth reddy, Jyothi Swaroop Reddy,
• Abstract summary: We present an open-source implementation of a smartphone-based gaze tracker that emulates the methodology proposed by a GooglePaper. Through the integration of machine learning techniques, we unveil an accurate eye tracking solution that is native to smartphones. Our findings exhibit the inherent potential to amplify eye movement research by significant proportions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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