Gesture Segmentation and Recognition Based on Kinect Depth Data

Authors

Yanming Mao, Department of Computer, Ningde Normal University, Ningde 352100, China;
Liliang Zhang, Department of Computer, Ningde Normal University, Ningde 352100, China;

Abstract

Abstract: Aiming at the problems that gesture recognition required high environmental background, segmented gesture usually contained wrist data and closing fingers easily caused false recognition, a gesture recognition method based on depth data was proposed. It captured gesture depth map, and it used Hands Generator to obtain the information of palm for gesture segmentation, in order to remove redundant wrist data, the constraint of the palm which looks like a square was added. The number of all the other four fingers except thumb could be acquired with the use of the scanline method, therefore, the width ratio of the adjacent fingers and the peculiarity of the thumb position were integrated to achieve the gesture recognition. Experimental results show that the average recognition rate of the method is 98.4%, and the method has good real-time performance and robustness in the different illumination conditions and complex backgrounds.

Recommended Citation

Mao, Yanming and Zhang, Liliang (2020) "Gesture Segmentation and Recognition Based on Kinect Depth Data," Journal of System Simulation: Vol. 27: Iss. 4, Article 22.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol27/iss4/22

First Page

830

Revised Date

2014-05-12

DOI Link

https://doi.org/

Last Page

835

CLC

TP391.41

Recommended Citation

Mao Yanming, Zhang Liliang. Gesture Segmentation and Recognition Based on Kinect Depth Data[J]. Journal of System Simulation, 2015, 27(4): 830-835.