# Robust Video-Based Eye Tracking Using Recursive Estimation of Pupil Characteristics

## TL;DR

This paper introduces a recursive estimation-based pupil detection method that leverages high frame rate video to improve accuracy, speed, and reliability in eye tracking, outperforming existing algorithms.

## Contribution

The novel recursive estimation approach exploits high-speed video to enhance pupil detection, segmentation, and classification in eye tracking applications.

## Key findings

- Higher detection rate than existing algorithms
- Improved accuracy in pupil localization
- Faster processing speed

## Abstract

Video-based eye tracking is a valuable technique in various research fields. Numerous open-source eye tracking algorithms have been developed in recent years, primarily designed for general application with many different camera types. These algorithms do not, however, capitalize on the high frame rate of eye tracking cameras often employed in psychophysical studies. We present a pupil detection method that utilizes this high-speed property to obtain reliable predictions through recursive estimation about certain pupil characteristics in successive camera frames. These predictions are subsequently used to carry out novel image segmentation and classification routines to improve pupil detection performance. Based on results from hand-labelled eye images, our approach was found to have a greater detection rate, accuracy and speed compared to other recently published open-source pupil detection algorithms. The program's source code, together with a graphical user interface, can be downloaded at https://github.com/tbrouns/eyestalker