Towards a human eye behavior model by applying Data Mining Techniques on Gaze Information from IEC
Denis Pallez (I3S), Laurent Brisson (I3S), Thierry Baccino (LPEQ)
TL;DR
This paper combines data mining and eye-tracking to model human eye behavior for visual optimization, using experiments on 80 individuals to inform parameterization.
Contribution
It introduces a novel approach integrating data mining with IEC and eye-tracking to model human eye behavior.
Findings
Data mining reveals key gaze patterns.
Effective parameterization of IEC based on gaze data.
Enhanced understanding of human visual attention.
Abstract
In this paper, we firstly present what is Interactive Evolutionary Computation (IEC) and rapidly how we have combined this artificial intelligence technique with an eye-tracker for visual optimization. Next, in order to correctly parameterize our application, we present results from applying data mining techniques on gaze information coming from experiments conducted on about 80 human individuals.
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Taxonomy
TopicsVideo Analysis and Summarization · Gaze Tracking and Assistive Technology · Data Visualization and Analytics