Abstract

Hand gesture is a natural way for people to communicate, it plays an important role in Human-Computer Interaction (HCI). Nowadays, many developers build HCI applications on the top of hand gesture recognition, but how to get more accurate when recognizing hand gestures still have a long way to go. The recent introduction of depth cameras like Leap Motion Controller (LMC) allows researchers to exploit the depth information to recognize hand gesture more robustly. This paper proposes a novel hand gesture recognition system with LMC for hand gesture recognition. Histogram of Oriented Gradient (HOG) feature is extracted from Leap Motion binarized and undistorted sensor images. We feed these features into a multi-class Support Vector Machine (SVM) classifier to recognize performed gesture. The results show that our model is much more accurate than previous work.

Full Text

Submitted Version (Free)

Published Version

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.