iCatcher: A Neural Network Approach for Automated Coding of Young Children’s Eye Movements

Infants’ 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’s results. Our method is publicly available as an open-source repository at https://github.com/yoterel/iCatcher