Real-time fingertip detection based on depth data

In this paper we propose a novel method to detect fingertip using depth data. The first step of our method is to segment hand from depth map precisely. Then a two layer hand model is constructed to detect self-occlusion and mitigate its impact. In the next step an extended graph model of hand is built to locate and label finger bases. Then we generate heat maps of finger bases to detect finger regions even fingers are closed or adhesion occurs. Finally fingertips are located on fingers by geodesic paths. Experiments on different finger motions and hand rotations show that our framework performs accurately when hand pose and rotation change. Compared with other approaches our method shows less errors and robust to depth noise.