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