Saliency-Based Gaze Visualization for Eye Movement Analysis

• Published in: Sensors
• Main Authors: Sangbong Yoo, Seongmin Jeong, Seokyeon Kim, Yun Jang
• Format: Article
• Language: English
• Published: MDPI AG 2021-07-01
• Subjects: gaze data visualization; saliency analysis; visual attention
• Online Access: https://www.mdpi.com/1424-8220/21/15/5178

Gaze movement and visual stimuli have been utilized to analyze human visual attention intuitively. Gaze behavior studies mainly show statistical analyses of eye movements and human visual attention. During these analyses, eye movement data and the saliency map are presented to the analysts as separate views or merged views. However, the analysts become frustrated when they need to memorize all of the separate views or when the eye movements obscure the saliency map in the merged views. Therefore, it is not easy to analyze how visual stimuli affect gaze movements since existing techniques focus excessively on the eye movement data. In this paper, we propose a novel visualization technique for analyzing gaze behavior using saliency features as visual clues to express the visual attention of an observer. The visual clues that represent visual attention are analyzed to reveal which saliency features are prominent for the visual stimulus analysis. We visualize the gaze data with the saliency features to interpret the visual attention. We analyze the gaze behavior with the proposed visualization to evaluate that our approach to embedding saliency features within the visualization supports us to understand the visual attention of an observer.