What is it about?
Webcam-based eye-tracking platforms have recently re-emerged due to improvements in machine learning-supported calibration processes and offer a scalable option for conducting eye movement studies. Although not yet comparable to the infrared-based ones regarding accuracy and frequency, some compelling performances have been observed. In this study, we test the reliability of webcam-based eye-tracking on a specific task: Eye movement distribution analysis for CVD (Colour Vision Deficiency) detection. We introduce a new publicly available eye movement dataset based on a pilot study (n=12) on images with dominant red colour (previously shown to be difficult with dichromatic AOI to investigate CVD by comparing attention patterns obtained in webcam eye-tracking sessions). We hypothesized that webcam eye tracking without infrared support could detect differing attention patterns between CVD and non-CVD participants and observed statistically significant differences, allowing the retention of our hypothesis.
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Why is it important?
The preliminary experiments on detecting CVD (Colour Vision Deficiency) with standard webcams in ordinary laptops pave the way to democratising eye-tracking for diagnostics.
Perspectives
Eye movements reveal lots of information. More subjects from all over the world should be involved in the experiments to overcome any possible cultural bias affecting eye movements towards some specific topics of interest.
Dr Alessandro Bruno
IULM University
Read the Original
This page is a summary of: Detecting colour vision deficiencies via Webcam-based Eye-tracking: A case study, May 2023, ACM (Association for Computing Machinery),
DOI: 10.1145/3588015.3590133.
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