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Related Experiment Video

Updated: Apr 7, 2026

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GazeCapsNet: A Lightweight Gaze Estimation Framework.

Shakhnoza Muksimova1, Yakhyokhuja Valikhujaev2, Sabina Umirzakova1

  • 1Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 461-701, Republic of Korea.

Sensors (Basel, Switzerland)
|February 26, 2025
PubMed
Summary

Mobile-GazeCapsNet offers efficient and accurate gaze estimation for mobile devices by integrating capsule networks with lightweight architectures. This novel framework achieves state-of-the-art performance with real-time processing capabilities.

Keywords:
capsule networkseye appearancegaze estimationlightweight architecturesself-attention routing mechanism

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Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Machine Learning

Background:

  • Gaze estimation is critical for VR, AR, and driver monitoring, but current models struggle with mobile deployment due to computational demands.
  • Existing methods often require complex pre-processing or extensive resources, limiting their use on mobile devices.

Purpose of the Study:

  • To introduce Mobile-GazeCapsNet, an efficient and accurate gaze estimation framework for mobile applications.
  • To overcome the limitations of existing models by leveraging capsule networks and lightweight architectures.

Main Methods:

  • Developed Mobile-GazeCapsNet by integrating capsule networks with MobileNet v2, MobileOne, and ResNet-18.
  • Implemented Self-Attention Routing (SAR) to replace iterative routing, dynamically allocating computational resources for improved efficiency.
  • Eliminated the need for facial landmark detection.

Main Results:

  • Achieved state-of-the-art (SOTA) performance on ETH-XGaze and Gaze360 datasets, with up to 15% reduction in Mean Angular Error (MAE).
  • Demonstrated real-time processing at 20 milliseconds per frame.
  • Required only 11.7 million parameters, making it suitable for resource-constrained environments.

Conclusions:

  • Mobile-GazeCapsNet offers a practical and effective solution for real-time mobile gaze estimation.
  • The framework sets a new standard for mobile gaze estimation technologies, balancing accuracy and efficiency.