Realtime Recognition of Dynamic Hand Gestures in Practical Applications

Abstract

Dynamic hand gesture acting as a semaphoric gesture is a practical and intuitive mid-air gesture interface. Nowadays benefiting from the development of deep convolutional networks, the gesture recognition has already achieved a high accuracy, however, when performing a dynamic hand gesture such as gestures of direction commands, some unintentional actions are easily misrecognized due to the similarity of the hand poses. This hinders the application of dynamic hand gestures and cannot be solved by just improving the accuracy of the applied algorithm on public datasets, thus it is necessary to study such problems from the perspective of human-computer interaction. In this article, two methods are proposed to avoid misrecognition by introducing activation delay and using asymmetric gesture design. First the temporal process of a dynamic hand gesture is decomposed and redefined, then a realtime dynamic hand gesture recognition system is built through a two-dimensional convolutional neural network. In order to investigate the influence of activation delay and asymmetric gesture design on system performance, a user study is conducted and experimental results show that the two proposed methods can effectively avoid misrecognition. The two methods proposed in this article can provide valuable guidance for researchers when designing realtime recognition system in practical applications.