# Unsupervised Learning of Eye Gaze Representation from the Web

## TL;DR

This paper introduces Ize-Net, an unsupervised deep learning model trained on a large web-sourced dataset to estimate eye gaze regions, demonstrating effective transfer to standard datasets and enhancing gaze estimation techniques.

## Contribution

The paper presents a novel self-supervised learning approach for eye gaze estimation using a large in-the-wild dataset, enabling effective transfer learning and improved gaze region classification.

## Key findings

- Ize-Net achieves competitive accuracy after fine-tuning on benchmark datasets.
- The learned features improve traditional machine learning methods for gaze estimation.
- Unsupervised training on web data reduces the need for labeled datasets.

## Abstract

Automatic eye gaze estimation has interested researchers for a while now. In this paper, we propose an unsupervised learning based method for estimating the eye gaze region. To train the proposed network "Ize-Net" in self-supervised manner, we collect a large `in the wild' dataset containing 1,54,251 images from the web. For the images in the database, we divide the gaze into three regions based on an automatic technique based on pupil-centers localization and then use a feature-based technique to determine the gaze region. The performance is evaluated on the Tablet Gaze and CAVE datasets by fine-tuning results of Ize-Net for the task of eye gaze estimation. The feature representation learned is also used to train traditional machine learning algorithms for eye gaze estimation. The results demonstrate that the proposed method learns a rich data representation, which can be efficiently fine-tuned for any eye gaze estimation dataset.