ABSTRACT
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, especially in those scenarios with medium-sized AOI (Areas of Interest) in images. 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.
- Alessandro Bruno, Francesco Gugliuzza, Edoardo Ardizzone, Calogero Carlo Giunta, and Roberto Pirrone. 2019. Image content enhancement through salient regions segmentation for people with color vision deficiencies. i-Perception 10, 3 (2019), 2041669519841073.Google Scholar
- Madhumitha Murali and Arzu Çöltekin. 2021. Conducting eye tracking studies online. In Proceedings of the Workshop on Adaptable Research Methods for Empirical Research with Map Users, Virtual Workshop, Vol. 6.Google Scholar
- Kilian Semmelmann and Sarah Weigelt. 2018. Online webcam-based eye tracking in cognitive science: A first look. Behavior Research Methods 50 (2018), 451–465.Google ScholarCross Ref
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