Abstract
Human intention is an internal, mental characterization for acquiring desired information. From interactive interfaces containing either <i>textual</i> or <i>graphical</i> 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., <i>Fixation Count, Eye Movement Ratio</i>) from the eye gaze data of 31 participants to generate a dataset containing 124 samples of visual intention for perceiving <i>textual</i> or <i>graphical</i> information, labeled as either <i>TEXT</i> or <i>IMAGE</i>, having 48.39% and 51.61% distribution, respectively. Using this dataset, we analyzed 5 classifiers, including <i>Support Vector Machine</i> (<i>SVM</i>) (<i>Accuracy</i>: 92.19%). Using the trained <i>SVM</i>, 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 <i>graphical</i> contents whereas older adults felt more interested in <i>textual</i> 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.
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