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Related Experiment Video

Updated: May 10, 2026

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Published on: November 7, 2014

Gait-based person identification robust to changes in appearance.

Yumi Iwashita¹, Koji Uchino, Ryo Kurazume

¹Department of Advanced Information Technology, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan. yumi@ieee.org

Sensors (Basel, Switzerland)

|June 21, 2013

Summary

This summary is machine-generated.

This study introduces a novel gait recognition method that robustly identifies individuals despite appearance changes. The technique enhances accuracy by weighting features from different body areas, improving person identification without needing extensive appearance databases.

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Last Updated: May 10, 2026

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Published on: November 7, 2014

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

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

Published on: August 12, 2019

Area of Science:

Biometrics
Computer Vision
Pattern Recognition

Background:

Gait recognition is challenged by variations in clothing and carried items.
Existing methods often require extensive databases of appearance changes, which are difficult to predict comprehensively.

Purpose of the Study:

To develop a robust gait identification method that overcomes appearance variability.
To avoid the need for pre-collected databases of predicted appearance alterations.

Main Methods:

The proposed method segments human body images into multiple regions.
Features are extracted from each region, and area-specific matching weights are calculated.
Identification is achieved through a weighted integration of similarities across all areas.

Main Results:

The novel method demonstrates robust person identification despite significant appearance changes.
Experimental results on the CASIA gait database show superior classification rates compared to conventional approaches.

Conclusions:

The proposed weighted feature integration method effectively handles appearance variations in gait recognition.
This approach offers a more practical and accurate solution for identifying individuals from gait images.