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

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

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Received: 14 January 2014; in revised form: 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.
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 which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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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.

AMA Style

Tang J, Luo J, Tjahjadi T, Gao Y. 2.5D Multi-View Gait Recognition Based on Point Cloud Registration. Sensors. 2014; 14(4):6124-6143.

Chicago/Turabian Style

Tang, Jin; Luo, Jian; Tjahjadi, Tardi; Gao, Yan. 2014. "2.5D Multi-View Gait Recognition Based on Point Cloud Registration." Sensors 14, no. 4: 6124-6143.



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