Poster abstract: evaluation of hidden Markov models robustness in uncovering focus of visual attention from noisy eye-tracker data

Abstract

Eye position, captured via an eye tracker, can uncover the focus of visual attention by classifying eye movements into fixations, pursuit or saccades [Duchowski 2003], with the former two indicating foci of visual attention. Such classification requires all other variability in eye tracking data, from sensor error to other eye movements (such as microsaccades, nystagmus and drifts) to accounted for effectively. The hidden Markov model provides a useful way of uncovering focus of visual attention from eye position when the user undertakes visually oriented tasks, allowing variability in eye tracking data to be modelled as a random variable.