Skeleton-based Dynamic Hand Gesture Recognition Using a Part-based GRU-RNN for Gesture-based Interface

• Published in: IEEE access Vol. 8; p. 1
• Main Authors: Shin, Seunghyeok, Kim, Whoi-Yul
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Gesture recognition; Man-machine interfaces; multi-layer neural network; Object recognition; Optimization; Recurrent neural networks
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2980128

Recent improvements in imaging sensors and computing units have led to the development of a range of image-based human-machine interfaces (HMIs). An important approach in this direction is the use of dynamic hand gestures for a gesture-based interface, and some methods have been developed to provide real-time hand skeleton generation from depth images for dynamic hand gesture recognition. Towards this end, we propose a skeleton-based dynamic hand gesture recognition method that divides geometric features into multiple parts and uses a gated recurrent unit-recurrent neural network (GRU-RNN) for each feature part. Because each divided feature part has fewer dimensions than an entire feature, the number of hidden units required for optimization is reduced. As a result, we achieved similar recognition performance as the latest methods with fewer parameters.