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2.5D multi-view gait recognition based on point cloud registration.

Jin Tang1, Jian Luo2, Tardi Tjahjadi3

  • 1School of Information Science and Engineering, Central South University, Changsha 410083, China. tjin@csu.edu.cn.

Sensors (Basel, Switzerland)
|April 2, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for human identification using 2.5D body modeling and gait analysis. The multi-view synthesizing method (MVSM) enhances view-invariant gait recognition, improving subject identification accuracy.

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Area of Science:

  • Computer Vision
  • Biometrics
  • Human-Computer Interaction

Background:

  • Human identification based on gait is a challenging biometric task.
  • Achieving view-invariant gait recognition requires robust feature extraction and modeling techniques.
  • Existing methods often struggle with variations in viewing angles and data dimensionality.

Purpose of the Study:

  • To propose a novel method for 2.5D human body modeling and gait feature extraction for subject identification.
  • To develop a multi-view synthesizing method based on point cloud registration (MVSM) for view-invariant gait recognition.
  • To introduce a Color Gait Curvature Image for dimensionality reduction and efficient gait analysis.

Main Methods:

  • Modeling of a 2.5D human body using point cloud data.
  • Development of a multi-view synthesizing method (MVSM) for generating training galleries.
  • Introduction of Color Gait Curvature Image for mapping 2.5D data to 2D space.
  • Application of Discrete Cosine Transform and 2D Principle Component Analysis for dimension reduction.
  • Gait recognition using a 2.5D view-invariant method based on point cloud registration.

Main Results:

  • The proposed method successfully models 2.5D human bodies and extracts gait features.
  • The multi-view synthesizing method (MVSM) significantly improves view-invariant gait recognition performance.
  • Experimental results on an in-house database demonstrate substantial performance gains with MVSM.

Conclusions:

  • The developed 2.5D gait recognition method offers a promising approach for human identification.
  • MVSM effectively addresses the challenge of view-invariance in gait recognition.
  • The Color Gait Curvature Image provides an efficient way to represent and analyze gait data.