Eye Tracking and Gesture Intelligent Systems Recognition System for Intelligent Human-Computer Interaction

Authors

  • Ke Wang School of Software Engineering, Jilin Technology College of Electronic Information, Jilin 132000, Jilin, China
  • Weihua Zhu School of Software Engineering, Jilin Technology College of Electronic Information, Jilin 132000, Jilin, China

Abstract

In response to the issue of decreased accuracy of eye tracking and gesture recognition systems under different lighting conditions, user postures, and hand occlusions, the aim of this study is to enhance the human-computer interaction experience in smart home scenarios and construct an intelligent human-computer interaction system. The system captures eye movement images using infrared ray sources and cameras, processes iris edges using Canny edge detection, and captures changes in the user’s posture using the Lucas-Kanade optical flow algorithm to improve the accuracy of eye tracking. Multi-scale convolutional neural network (CNN) layers are designed, and a self-attention mechanism is applied to enhance the precision
of hand feature extraction. Long short-term memory (LSTM) networks are used to improve the accuracy of dynamic gesture recognition and achieve a more natural and intuitive interaction mode. The experimental results show that the accuracy of eye tracking reaches 95% at a light intensity of 1000 lux, and that of gesture recognition is the lowest at 89% among all light intensity conditions tested. In the case of partial occlusion, the highest success rate of gesture recognition is 95%. After interferences such as head movement and high-frequency blinking are added, the success rates of eye tracking are 95% and 94%, respectively. The experimental results demonstrate the efficiency of the system under good lighting conditions and its robustness in capturing complex gestures and occlusion situations, showing the accuracy of the optimized system for smart home control.

Keywords: Intelligent Human-Computer Interaction, Eye Tracking, Gesture Recognition, Convolutional Neural Networks, Long Short-Term Memory

Cite As

K. Wang, W. Zhu, "Eye Tracking and Gesture Intelligent Systems Recognition System for Intelligent Human-Computer
Interaction", Engineering Intelligent Systems, vol. 33 no. 4, pp. 385-395, 2025.

 

Downloads

Published

2025-07-01

Issue

Vol. 33 No. 4 (2025): International Journal of Engineering Intelligent Systems

Section

Article

License

The submission of a paper implies that, if accepted for publication, it will not be published elsewhere in the same form, in any language, without the prior consent of the publisher. Before publication, authors are requested to assign copyright to CRL Publishing Ltd. This allows CRL to sanction photocopying, and to authorize the reprinting of issues or volumes according to demand. Authors' traditional rights will not be jeopardized by assigning Copyright in this way, as they retain the right to reuse the material following publication, and to veto third-party publication.