Unsupervised Clustering-Based Analysis of the Measured Eye-Tracking Data

Visual attention and its modeling are getting more and more focus during the past decades. It has been used for several years in various fields, such as the automotive industry, robotics, or even in diagnostic medicine. So far, the research has focused mainly on the generalization of the collected data, although on the contrary, the identification of unique features of the visual attention of the individuals remains an open research topic. The aim of this paper is to propose a methodology which is able to cluster people into groups based on individualities in their visual attention patterns. Unlike the former research approaches focused on the classification problem where the class of the subjects is required to be known, we focus our work on the open research problem of unsupervised machine learning based on the measured data about subjects' visual attention, solely. Our methodology is based on the clustering method which utilizes individual feature vectors created from measured visual attention data. Proposed feature vectors forming up the fingerprint of the attention of an individual are based on the direction of saccades of individuals. Our proposed methodology is designed to work with a limited set of the measured eye-tracking data without any additional information.