Saccade Landing Point Prediction

Abstract

A saccade is a fast eye movement that allows the change of visual fixation from one object of interest to another. These movements are characterized by very high angular velocity peaks that can reach up to 1,000o/s, making them as one of the fastest neuromotor activities in the human body. Modeling such a complex movement remains a challenge. Saccadic eye movements can be defined by initial and landing points, duration, amplitude, and velocity profile. The landing point is important as it defines the new fixation region and, therefore, the region of interest of the viewer. Its prediction may reduce problems caused by display-update latency in gaze-contingent systems that make real-time changes in the display based on eye tracking. The main contribution of this work is to propose the use of state-of-the-art machine learning techniques (i.e., Recurrent Neural Networks) for saccade landing point prediction in real-world scenarios. Our method was evaluated using 220,000 saccades from 75 subjects acquired during viewing video from Hollywood movies. The results obtained using our proposed methods outperform existing approaches with improvements of up to 40% error reduction. Our results show that dynamic temporal relationships exploited by Recurrent Neural Networks can improve the performance of traditional Feed Forward Neural Networks.