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Related Experiment Video

Updated: Jun 22, 2026

Video-oculography in Mice

Video-oculography in Mice

Published on: July 19, 2012

Pupil Localisation and Eye Centre Estimation Using Machine Learning and Computer Vision.

Wasiq Khan¹, Abir Hussain¹, Kaya Kuru²

¹Computer Science Department, Liverpool John Moores University, Liverpool L33AF, UK.

Sensors (Basel, Switzerland)

|July 10, 2020

Summary

This summary is machine-generated.

This study introduces a novel coarse-to-fine pupil localization method for improved accuracy in low-resolution images and challenging backgrounds. The approach enhances performance in various AI and industrial applications.

Keywords:

deep eye eye centre localisation eye gaze facial analysis image convolution iris detection machine intelligence pupil detection pupil segmentation

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Area of Science:

Computer Vision
Machine Learning
Image Processing

Background:

Existing pupil localization methods struggle with low-resolution images and variable backgrounds.
Accurate pupil detection is crucial for numerous AI and industrial applications.

Purpose of the Study:

To develop a robust coarse-to-fine pupil localization method.
To enhance pupil detection performance in challenging imaging conditions.

Main Methods:

Utilized a pre-trained model for facial landmark identification to isolate eye regions.
Employed multi-stage convolution with an adaptive kernel for pupil coordinate estimation.
Implemented a recursively calculated dynamic threshold for reliable candidate identification.

Main Results:

The proposed method demonstrated superior accuracy and reliability compared to existing techniques.
Performance was validated using standard metrics and a novel distance metric on multiple datasets.

Conclusions:

The developed pupil localization technique offers significant improvements over previous methods.
This advancement has broad applications in human-computer interaction, healthcare, and AI-driven analysis.