Visual Attention Saccadic Models Learn to Emulate Gaze Patterns From Childhood to Adulthood.

How people look at visual information reveals fundamental information about themselves, their interests and their state of mind. While previous visual attention models output static 2D saliency maps, saccadic models aim to predict not only where observers look at but also how they move their eyes to explore the scene. In this paper, we demonstrate that saccadic models are a flexible framework that can be tailored to emulate observer's viewing tendencies. More specifically, we use fixation data from 101 observers split into five age groups (adults, 8-10 y.o., 6-8 y.o., 4-6 y.o., and 2 y.o.) to train our saccadic model for different stages of the development of human visual system. We show that the joint distribution of saccade amplitude and orientation is a visual signature specific to each age group, and can be used to generate age-dependent scan paths. Our age-dependent saccadic model does not only output human-like, age-specific visual scan paths, but also significantly outperforms other state-of-the-art saliency models. We demonstrate that the computational modeling of visual attention, through the use of saccadic model, can be efficiently adapted to emulate the gaze behavior of a specific group of observers.