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Cross-View Gait Recognition Method Based on Multi-Teacher Joint Knowledge Distillation.

Ruoyu Li1,2, Lijun Yun1,2, Mingxuan Zhang3

  • 1College of Information, Yunnan Normal University, Kunming 650500, China.

Sensors (Basel, Switzerland)
|November 25, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces Multi-teacher Joint Knowledge Distillation (MJKD) to improve cross-view gait recognition. The method trains a lightweight model using insights from complex teacher models, achieving high accuracy with fewer parameters.

Keywords:
cross-view gait recognitionmulti-teacher joint knowledge distillationresnet

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Area of Science:

  • Computer Vision
  • Biometrics
  • Machine Learning

Background:

  • Cross-view gait recognition faces challenges with complex models, numerous parameters, and slow processing.
  • Existing methods struggle with efficient feature extraction and high accuracy across different viewpoints.

Purpose of the Study:

  • To develop an efficient and accurate cross-view gait recognition method.
  • To address the limitations of complex network models in gait recognition.
  • To enhance the feature extraction capabilities of lightweight models.

Main Methods:

  • Proposes Multi-teacher Joint Knowledge Distillation (MJKD) for gait recognition.
  • Utilizes multiple complex teacher models to extract and integrate inter-class relationships from single-view gait images.
  • Guides a lightweight student model's training using distilled knowledge to improve feature representation.

Main Results:

  • The MJKD-trained student model achieved 98.24% recognition accuracy on the CASIA_B dataset.
  • Significantly reduced the number of parameters and computational cost compared to benchmark models.
  • Demonstrated superior gait feature extraction and recognition performance.

Conclusions:

  • MJKD effectively improves cross-view gait recognition accuracy and efficiency.
  • The proposed method offers a viable solution for deploying gait recognition systems with reduced complexity.
  • Knowledge distillation is a powerful technique for enhancing lightweight models in biometrics.