Towards Hardware-Agnostic Gaze-Trackers

TL;DR

This paper introduces a deep learning-based, hardware-agnostic gaze-tracking system using ordinary RGB cameras, achieving high accuracy without calibration, aiming to make eye-controlled interfaces widely accessible.

Contribution

The authors develop a novel appearance-based deep neural network for constrained gaze-tracking that works across different hardware without calibration or device-specific tuning.

Findings

Achieved 1.8073cm error on GazeCapture dataset

No calibration or device-specific fine-tuning needed

Demonstrates potential for universal eye-controlled interfaces

Abstract

Gaze-tracking is a novel way of interacting with computers which allows new scenarios, such as enabling people with motor-neuron disabilities to control their computers or doctors to interact with patient information without touching screen or keyboard. Further, there are emerging applications of gaze-tracking in interactive gaming, user experience research, human attention analysis and behavioral studies. Accurate estimation of the gaze may involve accounting for head-pose, head-position, eye rotation, distance from the object as well as operating conditions such as illumination, occlusion, background noise and various biological aspects of the user. Commercially available gaze-trackers utilize specialized sensor assemblies that usually consist of an infrared light source and camera. There are several challenges in the universal proliferation of gaze-tracking as accessibility technologies, specifically its affordability, reliability, and ease-of-use. In this paper, we try to address these challenges through the development of a hardware-agnostic gaze-tracker. We present a deep neural network architecture as an appearance-based method for constrained gaze-tracking that utilizes facial imagery captured on an ordinary RGB camera ubiquitous in all modern computing devices. Our system achieved an error of 1.8073cm on GazeCapture dataset without any calibration or device specific fine-tuning. This research shows promise that one day soon any computer, tablet, or phone will be controllable using just your eyes due to the prediction capabilities of deep neutral networks.