HSFE Network and Fusion Model based Dynamic Hand Gesture Recognition

• Do Nhu Tai,
• In Seop Na,
• Soo Hyung Kim,

Vol. 14, No. 9, September 30, 2020
10.3837/tiis.2020.09.020, Download Paper (Free):

• HSFE network
• dynamic hand gesture
• hand detection
• Hand Gesture Recognition
• LSTM

Abstract

Dynamic hand gesture recognition(d-HGR) plays an important role in human-computer interaction(HCI) system. With the growth of hand-pose estimation as well as 3D depth sensors, depth, and the hand-skeleton dataset is proposed to bring much research in depth and 3D hand skeleton approaches. However, it is still a challenging problem due to the low resolution, higher complexity, and self-occlusion. In this paper, we propose a hand-shape feature extraction(HSFE) network to produce robust hand-shapes. We build a hand-shape model, and hand-skeleton based on LSTM to exploit the temporal information from hand-shape and motion changes. Fusion between two models brings the best accuracy in dynamic hand gesture (DHG) dataset.

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