Comparative Analysis of Eye-Tracking Solutions Using Deep Learning

Authors: Debmitra Ghosh, Aryan Kumar, Daniyal Raza, Ayaan Zafar, Avipsha Das, Palak Tamang

Published in: Journal of Computer Science and Engineering in Innovations and Research (ISSN No: 3049-1762 online)

Publication Date: May 15, 2025

📄 Abstract

Eye-tracking engineering science has emerged as a crucial tool in both research and various commercial applications. By monitoring and analyzing the direction and focus of an individual's gaze, eye-trackers provide insights into visual attention, cognitive processes, and user interaction.

This engineering plays a vital role in fields such as psychology, neuroscience, marketing, and user experience design, enabling professionals to interpret human behavior in nuanced ways. This paper presents a comprehensive comparative analysis of five prominent eye-tracking solutions: GazeRecorder, Tobii Pro Lab, Pupil Labs Core, iMotions, and Gazepoint.

The analysis focuses on hardware, use cases, accuracy, metrics, data analysis, remote testing, and cost. The results show that each solution has unique strengths and weaknesses, making them ideal for different applications. This study aims to guide researchers and professionals in selecting the most appropriate eye-tracking technology for their specific needs.

🔑 Keywords

Eye-tracking, GazeRecorder, Tobii Pro Lab, Pupil Labs Core, iMotions, Gazepoint