Skeleton-Based Dynamic Hand Gesture Recognition Using a Part-Based GRU-RNN for Gesture-Based Interface
Seung-Hyeok Shin, Whoi-Yul Kim
Abstract
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.