Gesture Control Using Active Difference Signatures and Sparse Learning

With the introduction of low cost depth sensors, gesture recognition systems for computer interface control are becoming a reality and require accurate recognition in real time. In this paper, we introduce a salient feature for gesture recognition called active difference signature, obtained by robust processing of depth maps and kinematic joint information. This feature is classified with variants of semi-supervised Linear extension of Graph Embedding and sparse representations to demonstrate advanced gesture control using depth cameras. Our active difference signature technique delivers highly accurate gesture recognition that is invariant to subject distance to camera, subject size, subject location within the camera field of view, and speed at which the subject performs the gestures.