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- Jin Tang, Jian Luo, Tardi Tjahjadi, and Yan Gao.
- School of Information Science and Engineering, Central South University, Changsha 410083, China. tjin@csu.edu.cn.
- Sensors (Basel). 2014 Jan 1;14(4):6124-43.
AbstractThis 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.
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