Hand gesture recognition using a skeleton-based feature representation with a random regression forest

Abstract

In this paper, we propose a method for automatic hand gesture recognition using a random regression forest with a novel set of feature descriptors created from skeletal data acquired from the Leap Motion Controller. The efficacy of our proposed approach is evaluated on the publicly available University of Padova Microsoft Kinect and Leap Motion dataset, as well as 24 letters of the English alphabet in American Sign Language. The letters that are dynamic (e.g. j and z) are not evaluated. Using a random regression forest to classify the features we achieve 100% accuracy on the University of Padova Microsoft Kinect and Leap Motion dataset. We also constructed an in-house dataset using the 24 static letters of the English alphabet in ASL. A classification rate of 98.36% was achieved on this dataset. We also show that our proposed method outperforms the current state of the art on the University of Padova Microsoft Kinect and Leap Motion dataset.