Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)

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MediaPipe Iris and Kalman Filter for Robust Eye Gaze Tracking

Authors
V. Ramesh1, *, O. K. Gowrishankar1, M. Namasivayam1, G. Mohanamurali2, N. Nanthakumaran2, V. R. Ranjithkumar2
1Assistant Professor, Department of Computer Science Engineering, K.S.R. College Engineering, Tiruchengode, Namakkal, India
2Department of Computer Science Engineering, K.S.R. College Engineering, Tiruchengode, Namakkal, India
*Corresponding author. Email: rameshapositive@gmail.com
Corresponding Author
V. Ramesh
Available Online 23 May 2025.
DOI
10.2991/978-94-6463-718-2_136How to use a DOI?
Keywords
MediaPipe Iris; Kalman Filters; eye gaze tracking; real-time iris tracking; human-computer interaction; augmented reality; virtual reality
Abstract

Eye gaze tracking is an important technology which has been utilized in many domains such as human-computer interaction, augmented reality, virtual reality, assistive technologies, etc. Here we present a simple but powerful approach using MediaPipe Iris in combination with Kalman Filters to improve eye gaze tracking. This integration combines fatigue and distraction models as well as a Kalman Filter (a predictive algorithm) with MediaPipe’s real-time iris tracking to provide the ability to accurately and consistently estimate gaze. This indicates resilience by both the system as a real-world effect functionality to challenges like lighting, partial occlusions, dynamic head movements. It also reduces computational costs so it can be used on mobile and embedded devices while providing flexibility for multiple applications. This approach lowers calibration needs and preserves privacy with native depth estimation and noise reduction, enabling its application in numerous fields, including education, healthcare, and gaming. This study showcases the system’s adaptability, accuracy, and practical applications, marking a significant advancement in the field of eye gaze tracking (EGT) technologies.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
Series
Advances in Computer Science Research
Publication Date
23 May 2025
ISBN
978-94-6463-718-2
ISSN
2352-538X
DOI
10.2991/978-94-6463-718-2_136How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - V. Ramesh
AU  - O. K. Gowrishankar
AU  - M. Namasivayam
AU  - G. Mohanamurali
AU  - N. Nanthakumaran
AU  - V. R. Ranjithkumar
PY  - 2025
DA  - 2025/05/23
TI  - MediaPipe Iris and Kalman Filter for Robust Eye Gaze Tracking
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 1630
EP  - 1641
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_136
DO  - 10.2991/978-94-6463-718-2_136
ID  - Ramesh2025
ER  -