Abstract
This paper presents a method for hand gesture recognition based on 3D point cloud. Digital image processing technology is used in this research. Based on the 3D point from depth camera, the system firstly extracts some raw data of the hand. After the data segmentation and preprocessing, three kinds of appearance features are extracted, including the number of stretched fingers, the angles between fingers and the gesture region’s area distribution feature. Based on these features, the system implements the identification of the gestures by using decision tree method. The results of experiment demonstrate that the proposed method is pretty efficient to recognize common gestures with a high accuracy.
Highlights
There has been a great emphasis lately on Human-Computer-Interaction (HCI) research to create easy-to-use interfaces by directly employing natural communication and manipulation skills of humans [1]
In order to validate the method proposed in this paper, we connected the SwissRanger 4000 (SR4000) depth camera with computer to do a lot of experiments
Gesture recognition has a wide range of applications in Human-Computer-Interaction
Summary
There has been a great emphasis lately on Human-Computer-Interaction (HCI) research to create easy-to-use interfaces by directly employing natural communication and manipulation skills of humans [1]. As an important part of body, naturally, the hand is given more and more attention. Gesture recognition is a key aspect of Human-Computer-Interaction. There are countless researches focus on this advanced topic to create natural user interface and to improve user experiences by using simple and intuitive hand gestures for free-hand controller [2]. How to detect the hands, segment them from the background and recognize the gestures become great challenges. Various methods are proposed to solve those issues
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