# Gaze Distribution Analysis and Saliency Prediction Across Age Groups

## TL;DR

This study investigates how visual attention and saliency prediction vary with age, analyzing eye movements across four age groups, and proposes an age-adapted computational model that improves prediction accuracy.

## Contribution

It introduces an age-adapted saliency model that accounts for age-related differences in visual attention, outperforming existing models across diverse age groups.

## Key findings

- Explorativeness varies with age, as shown by saliency map entropy.
- Saliency maps of the same age group better predict fixation points within that group.
- The age-adapted model outperforms existing models in saliency prediction.

## Abstract

Knowledge of the human visual system helps to develop better computational models of visual attention. State-of-the-art models have been developed to mimic the visual attention system of young adults that, however, largely ignore the variations that occur with age. In this paper, we investigated how visual scene processing changes with age and we propose an age-adapted framework that helps to develop a computational model that can predict saliency across different age groups. Our analysis uncovers how the explorativeness of an observer varies with age, how well saliency maps of an age group agree with fixation points of observers from the same or different age groups, and how age influences the center bias. We analyzed the eye movement behavior of 82 observers belonging to four age groups while they explored visual scenes. Explorativeness was quantified in terms of the entropy of a saliency map, and area under the curve (AUC) metrics was used to quantify the agreement analysis and the center bias. These results were used to develop age adapted saliency models. Our results suggest that the proposed age-adapted saliency model outperforms existing saliency models in predicting the regions of interest across age groups.