Abstract
Eye movements and changes in pupil dilation are known to provide information about viewer’s attention and interaction with visual content. This paper evaluates different statistical and signal processing methods for autonomously analysing pupil dilation signals and extracting information about viewer’s attention when perceiving visual information. In particular, using a commercial video-based eye tracker to estimate pupil dilation and gaze fixation, we demonstrate that wavelet-based signal processing provides an effective tool for pupil dilation analysis and discuss the effect that different image content has on pupil dilation and viewer’s attention.
Cite
CITATION STYLE
Elafoudi, G., Stankovic, V., Stankovic, L., Pappusetti, D., & Kalva, H. (2015). Evaluation of signal processing methods for attention assessment in visual content interaction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9281, pp. 580–588). Springer Verlag. https://doi.org/10.1007/978-3-319-23222-5_70
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