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Gait-ViT: Gait Recognition with Vision Transformer.

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This study introduces Gait-ViT, a novel gait recognition method using Vision Transformers. Gait-ViT effectively utilizes attention mechanisms for enhanced biometric identification, outperforming existing techniques.

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Area of Science:

  • Computer Science
  • Biometrics
  • Machine Learning

Background:

  • Biometric recognition identifies individuals via physical/behavioral traits.
  • Gait is a reliable biometric, perceivable from afar and hard to replicate.
  • Current methods often use Convolutional Neural Networks (CNNs), which lack attention mechanisms for focusing on critical image regions.

Purpose of the Study:

  • To introduce Gait-ViT, a Vision Transformer (ViT) based model for gait recognition.
  • To leverage the attention mechanism in ViT to improve feature extraction in gait analysis.
  • To demonstrate the superiority of ViT in gait recognition compared to existing CNN-based approaches.

Main Methods:

  • Gait energy images were generated by averaging images across the gait cycle.
  • Images were divided into patches, flattened, and embedded for sequence transformation.
  • Position and patch embeddings were applied, followed by Transformer encoder processing for gait representation.

Main Results:

  • The proposed Gait-ViT achieved exceptional accuracy: 99.93% on CASIA-B, 100% on OU-ISIR D, and 99.51% on OU-LP.
  • These results demonstrate the Vision Transformer's capability to surpass state-of-the-art gait recognition methods.
  • The attention mechanism in ViT proved effective in learning significant gait features.

Conclusions:

  • Vision Transformer (ViT) models, like Gait-ViT, are highly effective for gait recognition.
  • The attention mechanism is crucial for enhancing feature learning in gait analysis.
  • Gait-ViT represents a significant advancement in biometric recognition technology.