AITracker: A neural network designed for efficient and affordable eye tracking

Eye-tracking technology has long been a cornerstone in both academic and research fields, offering insights into behavior, cognition, and visual perception. That being said, however, its accessibility is hindered by the high costs and proprietary nature of existing methodologies. To address these issues, we present AITracker, an open-source application that works using only a standard webcam, leveraging a deep-learning model in order to provide accurate eye-tracking capabilities. Our solution offers flexibility and adaptability, enabling users to customize individual parameters in the software to meet their specific needs. Through robust data collection and neural network training, AITracker achieves incredibly fast response times with a high degree of accuracy, enabling gaze-tracking in up to eight directions, as well as blink detection. In order to better understand the impact of this technology in the context of existing solutions, this paper compares AITracker’s multi-layered neural network to various other prevalent eye-tracking methodologies. To that end, this paper also notes certain limitations that inhibit the software, including an undersized dataset and problematic distribution. Additionally, we explore various application scenarios, including hardware integration for assistive technology, hands-free gaming interfaces, advertising research, and attention monitoring in education. Moreover, feedback gathered from different users highlights the effectiveness and impact of AITracker across a diverse array of contexts.