Learning eye movement patterns for characterization of perceptual expertise
Proceedings of the Symposium on Eye Tracking Research and Applications
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Human saccade is a dynamic process of information pursuit. Based on the principle of information maximization, we propose a computational model to simulate human saccadic scanpaths on natural images. The model integrates three related factors as driven forces to guide eye movements sequentially - reference sensory responses, fovea-periphery resolution discrepancy, and visual working memory. For each eye movement, we compute three multi-band filter response maps as a coherent representation for the three factors. The three filter response maps are combined into multi-band residual filter response maps, on which we compute residual perceptual information (RPI) at each location. The RPI map is a dynamic saliency map varying along with eye movements. The next fixation is selected as the location with the maximal RPI value. On a natural image dataset, we compare the saccadic scanpaths generated by the proposed model and several other visual saliency-based models against human eye movement data. Experimental results demonstrate that the proposed model achieves the best prediction accuracy on both static fixation locations and dynamic scanpaths.