The recent introduction of depth cameras like Leap Motion Controller allows researchers to exploit the depth information to recognize hand gesture more robustly. This paper proposes a novel hand gesture recognition system with Leap Motion Controller. A series of features are extracted from Leap Motion tracking data, we feed these features along with HOG feature extracted from sensor images into a multi-class SVM classifier to recognize performed gesture, dimension reduction and feature weighted fusion are also discussed. Our results show that our model is much more accurate than previous work.
@article{arxiv.1711.04293,
title = {Hand Gesture Recognition with Leap Motion},
author = {Youchen Du and Shenglan Liu and Lin Feng and Menghui Chen and Jie Wu},
journal= {arXiv preprint arXiv:1711.04293},
year = {2017}
}