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Related Experiment Video

Updated: Jul 17, 2026

Video-oculography in Mice
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Video-oculography in Mice

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Webcam mouse using face and eye tracking in various illumination environments.

Yuan-Pin Lin1, Yi-Ping Chao, Chung-Chih Lin

  • 1Institute of Electrical Engineering, National Taiwan University.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|February 7, 2007
PubMed
Summary

This study presents a real-time face and eye tracking system using K-Nearest Neighbor (KNN) and an adaptive skin model. The system achieves over 92% accuracy in varied lighting, enabling webcam-based human-computer interaction.

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Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Machine Learning

Background:

  • Webcam-based human-computer interaction (HCI) is increasingly feasible due to enhanced computing power and webcam ubiquity.
  • Illumination variations significantly degrade the performance of skin-based face tracking systems, particularly on portable devices.

Purpose of the Study:

  • To develop an effective illumination recognition technique for robust real-time face tracking.
  • To enhance the stability and accuracy of human-computer interaction via webcam under diverse lighting conditions.

Main Methods:

  • Implemented a K-Nearest Neighbor (KNN) classifier combined with an adaptive skin model for illumination recognition.
  • Developed a real-time system for tracking user face and eye features.

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Three-Dimensional Mapping of the Rotation of Interactive Virtual Objects with Eye-Tracking Data

Published on: October 18, 2024

Related Experiment Videos

Last Updated: Jul 17, 2026

Video-oculography in Mice
09:43

Video-oculography in Mice

Published on: July 19, 2012

Eye Tracking Young Children with Autism
09:03

Eye Tracking Young Children with Autism

Published on: March 27, 2012

Three-Dimensional Mapping of the Rotation of Interactive Virtual Objects with Eye-Tracking Data
06:36

Three-Dimensional Mapping of the Rotation of Interactive Virtual Objects with Eye-Tracking Data

Published on: October 18, 2024

Main Results:

  • Achieved over 92% accuracy in face detection across various illumination environments using the KNN classifier.
  • Demonstrated successful real-time tracking of face and eye features at 15 frames per second on standard notebook platforms.
  • Developed a "Webcam Mouse" system for PC cursor control via face and eye tracking.

Conclusions:

  • The proposed illumination recognition technique effectively enhances the stability and accuracy of face tracking systems.
  • The developed "Webcam Mouse" system shows promising potential for consumer electronic applications, particularly in PC games.