Hand gesture recognition with depth data

Depth data acquired by current low-cost real-time depth cameras provide a very informative description of the hand pose, that can be effectively exploited for gesture recognition purposes. This paper introduces a novel hand gesture recognition scheme based on depth data. The hand is firstly extracted from the acquired depth maps with the aid also of color information from the associated views. Then the hand is segmented into palm and finger regions. Next, two different set of feature descriptors are extracted, one based on the distances of the fingertips from the hand center and the other on the curvature of the hand contour. Finally, a multi-class SVM classifier is employed to recognize the performed gestures. The proposed scheme runs in real-time and is able to achieve a very high accuracy on depth data acquired with the Kinect.