Benefits of temporal information for appearance-based gaze estimation

TL;DR

This paper investigates whether incorporating temporal sequences of eye images from high-resolution VR systems improves the accuracy of appearance-based gaze estimation models, especially for vertical gaze components.

Contribution

It demonstrates the significant benefits of using temporal gaze information in high-resolution, high-frame rate systems, enhancing model accuracy over static approaches.

Findings

Temporal information improves vertical gaze estimation accuracy.

High-resolution, high-frame rate data enhances gaze estimation.

Statistically significant improvements over static models.

Abstract

State-of-the-art appearance-based gaze estimation methods, usually based on deep learning techniques, mainly rely on static features. However, temporal trace of eye gaze contains useful information for estimating a given gaze point. For example, approaches leveraging sequential eye gaze information when applied to remote or low-resolution image scenarios with off-the-shelf cameras are showing promising results. The magnitude of contribution from temporal gaze trace is yet unclear for higher resolution/frame rate imaging systems, in which more detailed information about an eye is captured. In this paper, we investigate whether temporal sequences of eye images, captured using a high-resolution, high-frame rate head-mounted virtual reality system, can be leveraged to enhance the accuracy of an end-to-end appearance-based deep-learning model for gaze estimation. Performance is compared against a static-only version of the model. Results demonstrate statistically-significant benefits of temporal information, particularly for the vertical component of gaze.