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Facial feature point recognition method for human motion image using GNN.

Qingwei Wang1, Xiaolong Zhang2, Xiaofeng Li3

  • 1Harbin Huade University, Harbin 150025, China.

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|March 28, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a Genetic Neural Network (GNN) algorithm for robust facial feature point recognition in human motion. The method achieves high accuracy and efficiency, demonstrating strong anti-noise performance for real-world applications.

Keywords:
BP neural networkGenetic Neural Networkfacial detectionfacial feature pointshuman motion

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Biometrics

Background:

  • Facial feature point recognition faces challenges in clarity and efficiency during human motion.
  • Existing methods struggle with dynamic and varied human motion conditions.

Purpose of the Study:

  • To develop an effective facial feature point recognition method for human motion.
  • To improve the accuracy, efficiency, and anti-noise capabilities of facial recognition systems.

Main Methods:

  • Utilized a Genetic Neural Network (GNN) algorithm on the Hikey960 development board.
  • Employed an optimized BP neural network for image collection and classification.
  • Integrated genetic algorithms for neural network training and dynamic facial feature point transplantation.

Main Results:

  • Achieved facial feature point recognition accuracy and efficiency exceeding 85% in various human motion conditions.
  • Demonstrated good anti-noise performance with an average recall rate of approximately 90%.
  • The method exhibited short processing time, indicating high efficiency.

Conclusions:

  • The proposed GNN-based method significantly enhances facial feature point recognition for human motion.
  • The approach offers a valuable reference for advancements in human motion image recognition technology.
  • The system shows practical potential for applications requiring robust facial analysis under dynamic conditions.