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Sensors 2014, 14(4), 6124-6143; https://doi.org/10.3390/s140406124

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

1
School of Information Science and Engineering, Central South University, Changsha 410083, China
2
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)
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Abstract

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

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