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Spatial Attention-Based 3D Graph Convolutional Neural Network for Sign Language Recognition.

Muneer Al-Hammadi1,2, Mohamed A Bencherif1,3, Mansour Alsulaiman1,3

  • 1Centre of Smart Robotics Research (CS2R), King Saud University, Riyadh 11543, Saudi Arabia.

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Summary
This summary is machine-generated.

This study introduces an efficient sign language recognition system using a convolutional graph neural network (GCN). The GCN architecture, enhanced with spatial attention, improves gesture recognition for hearing-impaired communication.

Keywords:
attentiondeep learninggraph convolutional neural network (GCN)sign language recognition

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Sign language is crucial for hearing-impaired individuals' communication.
  • Mastering sign language is challenging for non-native users, creating a communication barrier.
  • Sign language recognition (SLR) technology aims to overcome these challenges.

Purpose of the Study:

  • To develop an efficient and effective architecture for sign language recognition.
  • To address the complexities of manual and non-manual parameters in sign language.
  • To bridge the communication gap between hearing-impaired and hearing individuals.

Main Methods:

  • Proposed an architecture based on a convolutional graph neural network (GCN).
  • Utilized a few separable 3D GCN layers for efficiency and to avoid over-smoothing.
  • Incorporated a spatial attention mechanism to enhance gesture representation.

Main Results:

  • The proposed GCN architecture demonstrated outstanding performance on various datasets.
  • The spatial attention mechanism effectively improved the representation of spatial contexts in gestures.
  • The limited-layer design mitigated the over-smoothing issue common in deep GCNs.

Conclusions:

  • The developed GCN-based architecture is efficient and effective for sign language recognition.
  • The integration of spatial attention significantly boosts recognition accuracy.
  • This approach offers a promising solution for improving communication accessibility for the hearing-impaired community.