Eye Tracking for Everyone

TL;DR

This paper introduces GazeCapture, a large-scale eye tracking dataset, and iTracker, a CNN model that enables real-time eye tracking on mobile devices without additional sensors, achieving high accuracy and broad applicability.

Contribution

The paper presents the first large-scale dataset for mobile eye tracking and a CNN model that achieves state-of-the-art accuracy in real-time on commodity hardware.

Findings

iTracker achieves 1.71cm prediction error without calibration on phones.

The model runs at 10-15fps on mobile devices.

Features learned generalize well to other datasets.

Abstract

From scientific research to commercial applications, eye tracking is an important tool across many domains. Despite its range of applications, eye tracking has yet to become a pervasive technology. We believe that we can put the power of eye tracking in everyone's palm by building eye tracking software that works on commodity hardware such as mobile phones and tablets, without the need for additional sensors or devices. We tackle this problem by introducing GazeCapture, the first large-scale dataset for eye tracking, containing data from over 1450 people consisting of almost 2.5M frames. Using GazeCapture, we train iTracker, a convolutional neural network for eye tracking, which achieves a significant reduction in error over previous approaches while running in real time (10-15fps) on a modern mobile device. Our model achieves a prediction error of 1.71cm and 2.53cm without calibration on mobile phones and tablets respectively. With calibration, this is reduced to 1.34cm and 2.12cm. Further, we demonstrate that the features learned by iTracker generalize well to other datasets, achieving state-of-the-art results. The code, data, and models are available at http://gazecapture.csail.mit.edu.