# Evaluation of Appearance-Based Methods and Implications for Gaze-Based Applications

## TL;DR

This paper evaluates the performance of appearance-based gaze estimation methods across various scenarios and introduces OpenGaze, a toolkit to facilitate their adoption in human-computer interaction applications.

## Contribution

It provides a comprehensive performance comparison of appearance-based gaze estimation methods and introduces OpenGaze, a software toolkit for enabling their use in HCI.

## Key findings

- Appearance-based methods perform variably across conditions.
- Calibration improves accuracy significantly.
- OpenGaze facilitates adoption of appearance-based gaze estimation.

## Abstract

Appearance-based gaze estimation methods that only require an off-the-shelf camera have significantly improved but they are still not yet widely used in the human-computer interaction (HCI) community. This is partly because it remains unclear how they perform compared to model-based approaches as well as dominant, special-purpose eye tracking equipment. To address this limitation, we evaluate the performance of state-of-the-art appearance-based gaze estimation for interaction scenarios with and without personal calibration, indoors and outdoors, for different sensing distances, as well as for users with and without glasses. We discuss the obtained findings and their implications for the most important gaze-based applications, namely explicit eye input, attentive user interfaces, gaze-based user modelling, and passive eye monitoring. To democratise the use of appearance-based gaze estimation and interaction in HCI, we finally present OpenGaze (www.opengaze.org), the first software toolkit for appearance-based gaze estimation and interaction.