  Accurate detection of fingertips in depth image is critical for
human-computer interaction. In this paper, we present a novel two-stream
convolutional neural network (CNN) for RGB-D fingertip detection. Firstly edge
image is extracted from raw depth image using random forest. Then the edge
information is combined with depth information in our CNN structure. We study
several fusion approaches and suggest a slow fusion strategy as a promising way
of fingertip detection. As shown in our experiments, our real-time algorithm
outperforms state-of-the-art fingertip detection methods on the public dataset
HandNet with an average 3D error of 9.9mm, and shows comparable accuracy of
fingertip estimation on NYU hand dataset.
