Bidirectional Independently Recurrent Neural Network for Skeleton-based Hand Gesture Recognition

Gestures are a common form of human communication and important for Human-Computer Interaction (HCI). In this paper, we propose a new approach for skeleton-based hand gesture recognition based on the Independently Recurrent Neural Network (IndRNN). First, a bidirectional IndRNN (Bi-IndRNN) is developed to extend the IndRNN with the capability of bidirectional processing. Then, a deep Bi-IndRNN network is constructed for gesture recognition, where, in addition to the joint coordinates, the temporal displacement of each joint is also used to enhance the input features. Experimental results demonstrate that the proposed method achieves the state-of-the-art performance on the widely used DHG dataset with an accuracy of 93.15% for the 14 gesture classes case and 91.13% for the 28 gesture classes case.