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Bi-stream Region Ensemble Network: Promoting Accuracy in Fingertip Localization from Stereo Images

Accurate fingertip localization is an important and challenging problem in Human Computer Interaction (HCI). Previous research on hand pose estimation are mainly based on depth images. However, due to the noise and missing values in depth images, it is difficult to improve the performance of fingertip localization. In this paper, we propose a novel scheme named Bi-stream Region Ensemble Network (Bi-REN) to estimate the locations of fingertips and the wrist from stereo images directly, without converting them into depth images. It first extracts feature maps using DenseNet structure in two streams from left and right images separately, and then regions of feature maps are cropped. After corresponding feature regions of the two streams are concatenated, they are fused by fully connected layers to predict the final joint positions. Our method achieves the highest accuracy in a publicly available dataset ThuHand17, with the mean error of the six joints (the wrist and five fingertips) as 8.98mm, reduced around 18% compared with the state-of-the-art method.