VIS-iTrack: Visual Intention Through Gaze Tracking Using Low-Cost Webcam

Human intention is an internal, mental characterization for acquiring desired information. From interactive interfaces containing either
textual
or
graphical
information, intention to perceive desired information is subjective and strongly connected with eye gaze. In this work, we determine such intention by analyzing real-time eye gaze data with a low-cost regular webcam. We extracted unique features (e.g.,
Fixation Count, Eye Movement Ratio
) from the eye gaze data of 31 participants to generate a dataset containing 124 samples of visual intention for perceiving
textual
or
graphical
information, labeled as either
TEXT
or
IMAGE
, having 48.39% and 51.61% distribution, respectively. Using this dataset, we analyzed 5 classifiers, including
Support Vector Machine
(
SVM
) (
Accuracy
: 92.19%). Using the trained
SVM
, we investigated the variation of visual intention among 30 participants, distributed in 3 age groups, and found out that young users were more leaned towards
graphical
contents whereas older adults felt more interested in
textual
ones. This finding suggests that real-time eye gaze data can be a potential source of identifying visual intention, analyzing which intention aware interactive interfaces can be designed and developed to facilitate human cognition.