Deformable deep convolutional generative adversarial network in microwave based hand gesture recognition system
Jiajun Zhang, Zhiguo Shi · 2017
Traditional vision-based hand gesture recognition systems is limited under dark circumstances. In this paper, we build a hand gesture recognition system based on microwave transceiver and deep learning algorithm. A Doppler radar sensor with dual receiving channels at 5.8GHz is used to acquire a big database of hand gestures signals. The received hand gesture signals are then processed with time-frequency analysis. Based on these big database of hand gesture, we propose a new classification architecture called deformable deep convolutional generative adversarial network. Experimental results shows the new architecture can upgrade the recognition rate by 10% and the deformable kernel can reduce the testing time cost by 30%.