Affiliations: [a] School of Science, Tianjin University of Technology and Education, Tianjin, China
| [b] Department of electrical and electronics Engineering, Hebei Petroleum University of Technology, Chengde, China
| [c] Shijiazhuang Posts and Telecommunications Technical College, Shijiazhuang, China
Correspondence:
[*]
Corresponding authors. Zhubing Hu, Department of electrical and electronics Engineering, Hebei Petroleum University of Technology, Chengde, China. E-mail: hubing9973316@163.com and Meiling Yao, Shijiazhuang Posts and Telecommunications Technical College, Shijiazhuang, China. E-mail: yml73125@126.com.
Abstract: Hand gesture recognition is important in human-computer interaction with wide applications in many fields. Different from common hand gesture recognition based on 2D images acquired from RGB camera, the utilization of 3D images provides additional spatial information about the target and attracts more and more attention in hand gesture recognition. However, most 3D images for hand gesture recognition are based on depth maps, which only take the distance information as a channel of 2D images, without taking full use of the 3D information. Besides, greater data volume of 3D images brings challenges to the arithmetic facility of hand gesture recognition. Here, we proposed a point cloud based method for hand gesture recognition. To fully use the 3D information, plane points for template matching were extracted based on their normal distributions, which leads to the average recognition rate over 97%. Pre-classification was implemented to ensure a high-efficient recognition without additional requirements for the computer. The proposed method may provide approach for accurate and efficient hand gesture recognition based on 3D images.
Keywords: Hand gesture recognition, point cloud, 3D images, template matching
