Dynamic Hand Pose Recognition Using Depth Data

Hand pose recognition has been a problem of great interest to the Computer Vision and Human Computer Interaction community for many years and the current solutions either require additional accessories at the user end or enormous computation time. These limitations arise mainly due to the high dexterity of human hand and occlusions created in the limited view of the camera. This work utilizes the depth information and a novel algorithm to recognize scale and rotation invariant hand poses dynamically. We have designed a volumetric shape descriptor enfolding the hand to generate a 3D cylindrical histogram and achieved robust pose recognition in real time.