  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.
