Finger detection and hand posture recognition based on depth information

In this work, we propose a novel framework for automatic finger detection and hand posture recognition, based mainly on depth information. Our method locates apex-shaped structures in a hand contour and deals efficiently with the challenging problem of partially merged fingers. Hand posture recognition is achieved using Fourier Descriptors of the contour, while global information about the fingers helps reducing the size of the search space. Our experiments on a dataset obtained from a Kinect device confirm the high recognition accuracy of our approach.