A Lightweight Hand Attitude Estimation Method Based on GCN Feature Enhancement

Abstract

In this study, a hand pose estimation method based on GCN feature enhancement is proposed to address the problem of the time-consuming nature and neglection of the internal relationships between hand joint points, which results in the low accuracy of hand pose estimation. Firstly, a lightweight feature extraction network RexNet is used, and deep separable convolutions are used instead of ordinary convolutions to reduce the model parameters and computational complexity. Secondly, deconvolution is added to the backend of the network to obtain preliminary estimation results of joint points. Finally, the GCN feature enhancement module is used to modify the preliminary estimation results to improve the accuracy of hand pose estimation. The proposed method is tested for accuracy on the CMU-Hand and RHD datasets. The results show that the proposed method achieves an AUC metric of 80.1% on the CMU-Hand dataset and 97.0% on the RHD dataset, and the accuracy of hand pose estimation is high.