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Multi-Biometric Feature Extraction from Multiple Pose Estimation Algorithms for Cross-View Gait Recognition.

Ausrukona Ray1, Md Zasim Uddin1, Kamrul Hasan1

  • 1Department of Computer Science and Engineering, Begum Rokeya University, Rangpur 5404, Bangladesh.

Sensors (Basel, Switzerland)
|December 17, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel multi-biometric framework for gait recognition, enhancing individual identification using multiple human pose estimation algorithms. The proposed method achieves state-of-the-art performance in skeleton-based cross-view gait recognition.

Keywords:
decision-level fusionfeature-level fusiongait recognitionhuman pose estimation algorithmresidual graph convolutional networkskeleton-based gait recognition

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Area of Science:

  • Biometrics
  • Computer Vision
  • Pattern Recognition

Background:

  • Gait recognition identifies individuals by walking patterns, crucial for long-distance identification.
  • Traditional appearance-based methods struggle with real-world variations (clothing, objects, illumination).
  • Model-based methods using skeletal key points offer robustness but historically underperform appearance-based methods.

Purpose of the Study:

  • To bridge the performance gap between skeleton-based and appearance-based gait recognition.
  • To introduce a multi-biometric framework leveraging multiple human pose estimation (HPE) algorithms.
  • To enhance the robustness and accuracy of skeleton-based gait recognition.

Main Methods:

  • Utilized state-of-the-art HPE algorithms (OpenPose, AlphaPose, HRNet) to generate diverse skeleton data from single videos.
  • Employed a residual graph convolutional network (ResGCN) for feature extraction from skeleton data.
  • Implemented feature-level fusion (FLF) and decision-level fusion (DLF) techniques.

Main Results:

  • The multi-biometric framework demonstrated superior skeleton-based cross-view gait recognition.
  • Achieved state-of-the-art performance on the CASIA-B dataset.
  • FLF aggregated features point-wise, while DLF used majority voting for final decisions.

Conclusions:

  • The proposed multi-biometric framework effectively enhances skeleton-based gait recognition accuracy.
  • Leveraging multiple HPE algorithms and fusion techniques overcomes limitations of single-source methods.
  • This approach offers a robust solution for real-world gait recognition challenges.