Robust features of finger regions based hand gesture recognition using Kinect sensor

Abstract

Thanks to the emergence of commercial depth cameras, e.g., Kinect, hand gesture recognition has attracted great attention in recent years. In this context, we present a novel Kinect based hand gesture recognition system which focuses on the features of finger regions. A hand cropping approach is proposed to extract the useful finger regions from a noisy hand image including palm, wrist and arm obtained by Kinect. Furthermore, an original dissimilarity metric, called Balanced Finger Earth Movers Distance (BFEMD), is used to classify hand gestures along with the hierarchical recognition strategy. Finally, the 12 popular gestures recognition experiments have been done to illustrate the effectiveness of the proposed gesture recognition system, and the experimental results show that the proposed system can achieve high recognition accuracy at a high speed.