Compact joints encoding for skeleton-based dynamic hand gesture recognition

Abstract

With the development of 3D hand pose estimation technologies, skeleton-based dynamic hand gesture recognition has attracted widespread attention. In this paper, we propose a novel framework for skeleton-based dynamic hand gesture recognition. In the spatial perception stream (SP-Stream), we design a compact joints encoding method. It can adaptively select compact joints based on the convex hull of the hand skeleton and encode them into a skeleton image for fully extracting spatial features. Besides, we present a global enhancement module (GEM) to enhance key feature maps. In the temporal perception stream (TP-Stream), we propose a motion perception module (MPM) to enhance the notable movement of hand gestures on X/Y/Z coordinate axes. Experimental results show that the proposed framework performs better than the state-of-the-art methods on two benchmark datasets.