DeepForest: 3D Hand Pose Estimation Using Deep Network and Random Forest Regression

Hand pose estimation plays an important role in human-computer interaction and virtual reality. In this paper, we present a regression framework to estimate 3D hand pose using depth image. Different from the previous methods, we propose a new method that has three key aspects: first, performance of system can be improved by setting up the better initial images using feature extraction via Convolution Neural Network (CNN); secondly, the error of joint position is estimated by dividing the dataset into groups of gesture type; thirdly, accuracy can be improved by learning the residual intensity of depth image by updating the residual of 3D joint coordinates constantly. It is noticed that importance of categorizing hand poses by gesture in computing the joint positions has been underestimated. Experimental evaluation with a public dataset A*STAR shows that our method produces low error of hand pose estimation and has more potential for the future work of the hand pose estimation.