  Fingertip detection plays an important role in human computer interaction.
Previous works transform binocular images into depth images. Then depth-based
hand pose estimation methods are used to predict 3D positions of fingertips.
Different from previous works, we propose a new framework, named Two-Stream
Binocular Network (TSBnet) to detect fingertips from binocular images directly.
TSBnet first shares convolutional layers for low level features of right and
left images. Then it extracts high level features in two-stream convolutional
networks separately. Further, we add a new layer: binocular distance
measurement layer to improve performance of our model. To verify our scheme, we
build a binocular hand image dataset, containing about 117k pairs of images in
training set and 10k pairs of images in test set. Our methods achieve an
average error of 10.9mm on our test set, outperforming previous work by 5.9mm
(relatively 35.1%).
