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Related Experiment Video

Updated: Jul 13, 2026

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder
06:54

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

Published on: March 4, 2018

General tensor discriminant analysis and gabor features for gait recognition.

Dacheng Tao1, Xuelong Li, Xindong Wu

  • 1Department of Computing, Hong Kong Polytechnic University, Kowloon. dacheng@dcs.bbk.ac.uk

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 19, 2007
PubMed
Summary

General Tensor Discriminant Analysis (GTDA) addresses the under-sample problem in image classification. This new method improves recognition rates for human gait recognition tasks by preserving discriminative information and offering stable performance.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Biometrics

Background:

  • Traditional image classification methods struggle with high-dimensional data and limited samples (under-sample problem).
  • Existing preprocessing techniques like Principal Component Analysis (PCA) and Two-Dimensional Linear Discriminant Analysis (2DLDA) have limitations in addressing the under-sample problem effectively.

Purpose of the Study:

  • To introduce General Tensor Discriminant Analysis (GTDA) as a novel preprocessing technique for Linear Discriminant Analysis (LDA).
  • To enhance human gait recognition accuracy by developing new Gabor filter-based image representations and applying GTDA for feature extraction.

Main Methods:

  • Developed General Tensor Discriminant Analysis (GTDA) to reduce the under-sample problem and preserve discriminative information.

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Last Updated: Jul 13, 2026

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  • Introduced three Gabor function-based image representations: GaborD, GaborS, and GaborSD.
  • Validated GTDA and Gabor-based representations using human gait recognition on the USF HumanID Database.

    • Main Results:

    • GTDA demonstrated improved stability and effectiveness compared to PCA and 2DLDA.
    • The proposed Gabor-based representations combined with GTDA and LDA achieved good performance in human gait recognition.
    • Experimental comparisons showed competitive results against nine state-of-the-art classification methods.

    Conclusions:

    • GTDA is an effective preprocessing step for LDA, particularly for high-dimensional data with limited samples.
    • Gabor-based image representations offer a promising approach for human gait recognition.
    • The integrated GTDA and Gabor-based methods provide a robust solution for biometric identification tasks.