Hand pose estimation from single depth image has achieved great progress in recent years, however, up-to-data methods are still not satisfying the application requirements like in human-computer interaction. One possible reason is that existing methods try to learn a general regression function for all types of hand depth images. To handle this problem, we propose a novel "divide-and-conquer" method, which includes a classification step and a regression step. At first, a convolu-tional neural network classifier is used to classify the input hand depth image into different types. Then, an effective and efficient multiway cascaded random forest regressor is used to estimate the hand joints' 3D positions. Experiments demonstrate that the proposed method achieves state-of-the-art performance on challenging dataset. Moreover, the proposed method can be easily combined with other regression method.
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