Abstract: In most vision tasks related to human hands,such as human computer interaction and sign language recognition,hand detection is a distinctly important preprocessing phase.With the development of RGB-D data acquisition equipment,the extra depth data can complement the color data effectively,so they can provide more powerful feature representation.The traditional detection methods based on hand-crafted features(skin color or HOG) cannot form a well hand representation.While a lot of detection methods based on deep learning can avoid such weakness by learning effective features from data.To combine the advantages of RGB-D data and deep learning,a two-stream Faster R-CNN detection framework was proposed in this paper.The proposed method adds an extra depth stream information,and combines it with RGB stream information in the feature level.The experiment results show that the proposed method can achieve a higher detection precision than the Faster R-CNN framework which uses RGB or fuses the RGB and Depth in the data level.Thus,the proposed method can fuse the color and depth data effectively,and improve the performance of hand detection.

Key words: Hand detection,Depth data,Deep learning,Two-stream Faster R-CNN