Dynamic Gesture Recognition Based on Deep Learning in Human-to-Computer Interfaces

Abstract

ABSTRACT Currently, gesture recognition provides a faster, simpler, convenient, effective and more natural way for human-computer interaction, which has been widely concerned. Gesture recognition plays an important role in real life. The manual feature extraction in traditional gesture recognition methods is time-consuming and strenuous. Moreover, in order to improve the accuracy of recognition, the quantity and quality of features to be extracted are required to be very high, which is a bottleneck for traditional gesture recognition methods. Therefore, we propose a deep learning method for dynamic gesture recognition in Human-to-Computer interfaces. An improved inverted residual network architecture is utilized as the basis of SSD (Single Shot MultiBox Detector) network for feature extraction. And the convolution structure of the auxiliary layer is predicted by using the inverse residual structure combining the cavity convolution. It uses multi-scale information, which can reduce the amount of calculation and parameters number. Transfer learning is used to optimize the trained network model so as to reduce the training time and make the model more convergent. Finally, experimental results show that the proposed method can recognize different gestures quickly and effectively.