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Updated: May 31, 2026

Paw-Print Analysis of Contrast-Enhanced Recordings (PrAnCER): A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits
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Paw-Print Analysis of Contrast-Enhanced Recordings (PrAnCER): A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits

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Human gait recognition using patch distribution feature and locality-constrained group sparse representation.

Dong Xu1, Yi Huang, Zinan Zeng

  • 1School of Computer Engineering, Nanyang Technological University, Singapore.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|July 5, 2011
PubMed
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We introduce Gabor-PDF, a novel patch distribution feature for human gait recognition. This method, combined with locality-constrained group sparse representation (LGSR), significantly improves recognition accuracy on benchmark datasets.

Area of Science:

  • Computer Vision
  • Biometrics
  • Pattern Recognition

Background:

  • Human gait recognition is crucial for security and surveillance.
  • Existing methods face challenges with variations in gait patterns and data quality.
  • Efficient feature representation and robust classification are key.

Purpose of the Study:

  • To propose a novel feature representation, Gabor-PDF, for enhanced human gait recognition.
  • To introduce a new classification method, locality-constrained group sparse representation (LGSR), for improved accuracy.
  • To validate the effectiveness of Gabor-PDF and LGSR on a benchmark dataset.

Main Methods:

  • Representing gait energy images (GEIs) using local augmented Gabor features (Gabor-PDF).
  • Utilizing a global Gaussian mixture model (GMM) as a universal background model.

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Paw-Print Analysis of Contrast-Enhanced Recordings (PrAnCER): A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits
06:25

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Published on: August 12, 2019

Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

  • Employing LGSR with enforced group sparsity and local smooth sparsity constraints for classification.

    • Main Results:

    • Gabor-PDF effectively captures discriminative gait information.
    • LGSR demonstrates superior performance compared to standard group sparse representation.
    • The combined Gabor-PDF and LGSR achieved state-of-the-art results on the USF HumanID database.

    Conclusions:

    • Gabor-PDF and LGSR are effective for human gait recognition.
    • The proposed methods offer significant improvements in recognition rates.
    • This work advances the field of biometric identification through gait analysis.