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