Abstract
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.
Original language | English (US) |
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Pages (from-to) | 765-779 |
Number of pages | 15 |
Journal | Infancy |
Volume | 27 |
Issue number | 4 |
DOIs | |
State | Published - Jul 1 2022 |
All Science Journal Classification (ASJC) codes
- Developmental and Educational Psychology
- Pediatrics, Perinatology, and Child Health