Dataset for eye-tracking tasks

TL;DR

This paper introduces a new dataset of 10,000 annotated eye images designed to facilitate training convolutional neural networks specifically for eye-tracking tasks, addressing the lack of suitable datasets.

Contribution

The paper provides a dedicated dataset for eye-tracking, enabling custom model training and improving accuracy for iris detection tasks.

Findings

Dataset contains 10,000 annotated eye images.

Annotations include eye coordinates and radius.

Dataset supports training of specialized eye-tracking models.

Abstract

In recent years many different deep neural networks were developed, but due to a large number of layers in deep networks, their training requires a long time and a large number of datasets. Today is popular to use trained deep neural networks for various tasks, even for simple ones in which such deep networks are not required. The well-known deep networks such as YoloV3, SSD, etc. are intended for tracking and monitoring various objects, therefore their weights are heavy and the overall accuracy for a specific task is low. Eye-tracking tasks need to detect only one object - an iris in a given area. Therefore, it is logical to use a neural network only for this task. But the problem is the lack of suitable datasets for training the model. In the manuscript, we presented a dataset that is suitable for training custom models of convolutional neural networks for eye-tracking tasks. Using data set data, each user can independently pre-train the convolutional neural network models for eye-tracking tasks. This dataset contains annotated 10,000 eye images in an extension of 416 by 416 pixels. The table with annotation information shows the coordinates and radius of the eye for each image. This manuscript can be considered as a guide for the preparation of datasets for eye-tracking devices