Abstract

We present a study of an artificial neural architecture that predict human ocular scanpaths while they are free-viewing different images types. This analysis is made by comparing different metrics that encompass scanpath patterns, these metrics aim to measure spatial and temporal errors; such as the MSE, ScanMatch, cross-correlogram peaks, and MultiMatch. Our methodology begin by choosing one architecture and training different parametric models per subject and image type, this allows to adjust the models to each person and a given set of images. We find out that there is a clear difference in prediction when people free-view images with high visual content (high-frequency contents) and low visual content (no-frequency contents). The input features selected for predicting the scanpath are saliency maps calculated from foveated images together with the past of the ocular scanpath of subjects, modeled by our architecture called FovSOS-FSD (Foveated Saliency and Ocular Scanpath with Feature Selection and Direct Prediction).The results of this study could be used to improve the design of gaze-controlled interfaces, virtual reality, as well as to better understand how humans visually explore their surroundings and pave a way to make future research.