Next Article in Journal
A New Approach to Integrate Internet-of-Things and Software-as-a-Service Model for Logistic Systems: A Case Study
Previous Article in Journal
A Pseudolite-Based Positioning System for Legacy GNSS Receivers
Article Menu

Export Article

Open AccessArticle
Sensors 2014, 14(4), 6124-6143;

2.5D Multi-View Gait Recognition Based on Point Cloud Registration

School of Information Science and Engineering, Central South University, Changsha 410083, China
School of Engineering, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK
Author to whom correspondence should be addressed.
Received: 14 January 2014 / Revised: 24 March 2014 / Accepted: 24 March 2014 / Published: 28 March 2014
(This article belongs to the Section Physical Sensors)
Full-Text   |   PDF [1108 KB, uploaded 21 June 2014]


This paper presents a method for modeling a 2.5-dimensional (2.5D) human body and extracting the gait features for identifying the human subject. To achieve view-invariant gait recognition, a multi-view synthesizing method based on point cloud registration (MVSM) to generate multi-view training galleries is proposed. The concept of a density and curvature-based Color Gait Curvature Image is introduced to map 2.5D data onto a 2D space to enable data dimension reduction by discrete cosine transform and 2D principle component analysis. Gait recognition is achieved via a 2.5D view-invariant gait recognition method based on point cloud registration. Experimental results on the in-house database captured by a Microsoft Kinect camera show a significant performance gain when using MVSM. View Full-Text
Keywords: gait; person identification; 2.5D modeling; point cloud registration gait; person identification; 2.5D modeling; point cloud registration
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Share & Cite This Article

MDPI and ACS Style

Tang, J.; Luo, J.; Tjahjadi, T.; Gao, Y. 2.5D Multi-View Gait Recognition Based on Point Cloud Registration. Sensors 2014, 14, 6124-6143.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top