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

Two-stream convolutional neural network for accurate RGB-D fingertip detection using depth and edge information