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Advances in Vision-Based Gait Recognition: From Handcrafted to Deep Learning.

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Area of Science:

  • Biometrics
  • Computer Science
  • Artificial Intelligence

Background:

  • Behavioral biometrics, specifically gait recognition, is crucial for identity verification.
  • Traditional handcrafted methods for gait recognition face challenges due to environmental and sensor variations (covariates).
  • Deep learning offers a promising solution to enhance the accuracy and robustness of gait recognition systems.

Purpose of the Study:

  • To provide a comprehensive overview of deep learning-based gait recognition techniques.
  • To summarize the performance of these advanced methods across various gait datasets.
  • To highlight the potential of deep learning in advancing the field of biometrics.

Main Methods:

  • Review of existing literature on deep learning architectures applied to gait recognition.
  • Analysis of studies employing convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers for gait analysis.
  • Compilation of performance metrics reported on benchmark gait datasets.

Main Results:

  • Deep learning models demonstrate superior performance compared to traditional methods in gait recognition.
  • These models effectively handle variations in walking style, viewpoint, and clothing.
  • Performance varies across different datasets, necessitating tailored model selection.

Conclusions:

  • Deep learning is a powerful tool for robust and accurate gait recognition.
  • Further research can focus on optimizing models for real-world, unconstrained environments.
  • The findings support the adoption of deep learning in next-generation biometric systems.