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Gait recognition using radon transform and linear discriminant analysis.

Nikolaos V Boulgouris1, Zhiwei X Chi

  • 1Department of Electronic Engineering, Division of Engineering, King's College London, WC2R 2LS London, U.K. nikolaos.boulgouris@kcl.ac.uk

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|March 16, 2007
PubMed
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A novel feature extraction method using the Radon transform improves gait recognition accuracy. This new system significantly outperforms existing methods for identifying individuals by their walking patterns.

Area of Science:

  • Biometrics
  • Computer Vision
  • Pattern Recognition

Background:

  • Gait recognition is a challenging biometric identification method.
  • Existing methods face limitations in accuracy and efficiency.
  • Robust feature extraction is crucial for effective gait analysis.

Purpose of the Study:

  • To propose a new feature extraction process for gait representation and recognition.
  • To enhance the performance of gait recognition systems.
  • To develop a low-dimensional feature vector for efficient gait description.

Main Methods:

  • Utilizing the Radon transform on binary silhouettes for feature extraction.
  • Computing gait templates from transformed silhouettes.
  • Applying linear discriminant analysis and subspace projection for dimensionality reduction.

Related Experiment Videos

  • Describing gait sequences with low-dimensional Radon template coefficients.

    • Main Results:

    • Achieved considerable improvements in gait recognition performance.
    • Demonstrated superior results compared to state-of-the-art methods on the Gait Challenge database.
    • Successfully described gait sequences using compact feature vectors.

    Conclusions:

    • The proposed Radon transform-based feature extraction method is highly effective for gait recognition.
    • This approach offers significant advantages over existing techniques.
    • The system provides a robust and efficient solution for biometric identification based on gait.