@article{173436,
  author = {Yotam Erel and Christine Potter and Sagi Jaffe-Dax and Casey Lew-Williams and Amit Bermano},
  title = {iCatcher: A neural network approach for automated coding of young children{\textquoteright}s eye movements.},
  abstract = {<p>Infants{\textquoteright} looking behaviors are often used for measuring attention, real-time processing, and learning-often using low-resolution videos. Despite the ubiquity of gaze-related methods in developmental science, current analysis techniques usually involve laborious post hoc coding, imprecise real-time coding, or expensive eye trackers that may increase data loss and require a calibration phase. As an alternative, we propose using computer vision methods to perform automatic gaze estimation from low-resolution videos. At the core of our approach is a neural network that classifies gaze directions in real time. We compared our method, called iCatcher, to manually annotated videos from a prior study in which infants looked at one of two pictures on a screen. We demonstrated that the accuracy of iCatcher approximates that of human annotators and that it replicates the prior study{\textquoteright}s results. Our method is publicly available as an open-source repository at https://github.com/yoterel/iCatcher.</p>
},
  year = {2022},
  journal = {Infancy : the official journal of the International Society on Infant Studies},
  volume = {27},
  pages = {765-779},
  month = {12/2022},
  issn = {1532-7078},
  doi = {10.1111/infa.12468},
  language = {eng},
}