| Autor: |
Niklas Zdarsky, Stefan Treue, Moein Esghaei |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
Frontiers in Human Neuroscience, Vol 15 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
1662-5161 |
| DOI: |
10.3389/fnhum.2021.685830 |
| Popis: |
Real-time gaze tracking provides crucial input to psychophysics studies and neuromarketing applications. Many of the modern eye-tracking solutions are expensive mainly due to the high-end processing hardware specialized for processing infrared-camera pictures. Here, we introduce a deep learning-based approach which uses the video frames of low-cost web cameras. Using DeepLabCut (DLC), an open-source toolbox for extracting points of interest from videos, we obtained facial landmarks critical to gaze location and estimated the point of gaze on a computer screen via a shallow neural network. Tested for three extreme poses, this architecture reached a median error of about one degree of visual angle. Our results contribute to the growing field of deep-learning approaches to eye-tracking, laying the foundation for further investigation by researchers in psychophysics or neuromarketing. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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