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Facial Gesture Detection for Individuals with Reduced Hand Mobility Using Graph Neural Networks

  • Universidad Peruana de Ciencias Aplicadas

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper addresses the limited digital accessibility faced by individuals with reduced hand mobility and explores how to overcome it. An efficient facial gesture recognition model is proposed, designed to operate based on facial landmarks (Facial Mesh) extracted using Mediapipe’s Face Mesh technology. The model incorporates a graph neural network (GNN) with a lightweight architecture and low computational complexity to detect facial gestures on the user’s face, enabling smooth execution on low-performance computers without significantly compromising user experience. The work presented may be of interest to researchers focused on digital accessibility for individuals with motor impairments in their hands.

Original languageEnglish
Title of host publicationComputer-Human Interaction Research and Applications - 9th International Conference, CHIRA 2025, Proceedings
EditorsJosef F. Krems, Hugo Plácido da Silva, Pietro Cipresso
PublisherSpringer Science and Business Media Deutschland GmbH
Pages129-148
Number of pages20
ISBN (Print)9783032164506
DOIs
StatePublished - 2026
Event9th International Conference on Computer-Human Interaction Research and Applications, CHIRA 2025 - Marbella, Spain
Duration: 20 Oct 202521 Oct 2025

Publication series

NameCommunications in Computer and Information Science
Volume2835 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference9th International Conference on Computer-Human Interaction Research and Applications, CHIRA 2025
Country/TerritorySpain
CityMarbella
Period20/10/2521/10/25

Keywords

  • Assistive Technology
  • Eye Tracking
  • Facial Gesture Recognition
  • Graph Neural Network
  • Human-Computer
  • Interaction

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