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Development of vision based multiview gait recognition system with MMUGait database.

Hu Ng1, Wooi-Haw Tan1, Junaidi Abdullah1

  • 1Faculty of Computing and Informatics, Multimedia University, 63100 Cyberjaya, Malaysia.

Thescientificworldjournal
|August 22, 2014
PubMed
Summary
This summary is machine-generated.

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A new gait recognition system achieves over 90% accuracy using the MMUGait database. This multiview model-based approach effectively handles various walking conditions and occlusions, outperforming existing methods.

Area of Science:

  • Biometrics
  • Computer Vision
  • Pattern Recognition

Background:

  • Gait recognition is a challenging biometric modality due to variations in walking patterns and environmental factors.
  • Existing gait recognition systems often struggle with occlusions and diverse walking trajectories.

Purpose of the Study:

  • To introduce the MMUGait database, a new resource for gait recognition research.
  • To propose and evaluate a novel multiview model-based gait recognition system with joint detection.

Main Methods:

  • Developed the MMUGait database with 82 subjects under normal conditions and 19 under covariate factors, captured from two views.
  • Proposed a multiview model-based system employing joint detection for gait recognition.
  • Implemented silhouette enhancement, joint angular trajectory determination, feature extraction (crotch height, step-size), smoothing, normalization, and feature selection.

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Related Experiment Videos

Last Updated: Apr 25, 2026

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06:17

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Published on: April 3, 2026

105
Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder
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Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults
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Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

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Main Results:

  • The proposed system achieved a correct classification rate exceeding 90% on both the MMUGait and SOTON Small DB datasets.
  • The system demonstrated robust performance across different walking trajectories and covariate factors, including occlusions.
  • The approach outperformed other methods on the SOTON Small DB in most experimental cases.

Conclusions:

  • The proposed multiview model-based gait recognition system is effective and robust, particularly under challenging conditions.
  • The MMUGait database provides a valuable resource for advancing gait recognition research.
  • The joint detection approach enhances performance in real-world scenarios with occlusions and trajectory variations.