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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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FGFF Descriptor and Modified Hu Moment-Based Hand Gesture Recognition.

Beiwei Zhang1, Yudong Zhang2, Jinliang Liu1

  • 1School of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China.

Sensors (Basel, Switzerland)
|October 13, 2021
PubMed
Summary

This study introduces a new feature descriptor for hand gesture recognition, improving accuracy and efficiency. The Fingertip Gradient orientation with Finger Fourier (FGFF) descriptor enhances performance in complex gesture recognition tasks.

Keywords:
FGFF descriptorHu moment invariantsfinger thicknesshand gesture recognitionweighted AdaBoost classifier

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Pattern Recognition

Background:

  • Hand gesture recognition is a long-standing challenge in computer science.
  • Existing methods suffer from insufficient features, leading to poor performance and robustness.
  • Developing comprehensive and discriminative features is crucial for advancing gesture recognition.

Purpose of the Study:

  • To propose a novel and effective feature descriptor for hand gesture recognition.
  • To enhance the accuracy and robustness of gesture recognition systems.
  • To introduce a new approach for representing hand gestures using combined features.

Main Methods:

  • A novel Fingertip Gradient orientation with Finger Fourier (FGFF) descriptor was developed.
  • Modified Hu moment invariants were employed to encode structural information.
  • A weighted AdaBoost classifier integrated finger-earth mover's distance and SVM models.

Main Results:

  • The proposed FGFF descriptor and modified Hu moments demonstrated superior performance.
  • Experiments on a ten-gesture dataset showed significant improvements in recognition accuracy.
  • The method proved to be efficient and robust compared to benchmark algorithms.

Conclusions:

  • The developed FGFF descriptor offers a comprehensive and discriminative representation for hand gestures.
  • The integration of gradient and Fourier features enhances gesture recognition capabilities.
  • This approach provides a promising solution for accurate and efficient hand gesture recognition.