  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.
